@article {43139,
	title = {Approximate Bayesian Inference Based on Expected Evaluation},
	journal = {Bayesian Analysis},
	volume = {1},
	year = {2023},
	month = {Jan-01-2023},
	publisher = {Project euclid},
	abstract = {Approximate Bayesian computing (ABC) and Bayesian Synthetic likelihood (BSL) are two popular families of methods to evaluate the posterior distribution when the likelihood function is not available or tractable. For existing variants of ABC and BSL, the focus is usually first put on the simulation algorithm, and after that the form of the resulting approximate posterior distribution comes as a consequence of the algorithm. In this paper we turn this around and firstly define a reasonable approximate posterior distribution by studying the distributional properties of the expected discrepancy, or more generally an expected evaluation, with respect to generated samples from the model. The resulting approximate posterior distribution will be on a simple and interpretable form compared to ABC and BSL.Secondly a Markov chain Monte Carlo (MCMC) algorithm is developed to simulate from the resulting approximate posterior distribution. The algorithm was evaluated on a synthetic data example and on the Stepping Stone population genetics model, demonstrating that the proposed scheme has real world applicability. The algorithm demonstrates competitive results with the BSL and sequential Monte Carlo ABC algorithms, but is outperformed by the ABC MCMC.},
	doi = {10.1214/23-BA1368},
	url = {https://projecteuclid.org/journals/bayesian-analysis/volume--1/issue--1/Approximate-Bayesian-Inference-Based-on-Expected-Evaluation/10.1214/23-BA1368.full},
	author = {Hugo Lewi {Hammer} and Riegler, Michael and Tjelmeland, H{\r a}kon}
}
@inproceedings {42498,
	title = {Detecting human embryo cleavage stages using YOLO v5 object detection algorithm},
	journal = {Nordic Artificial Intelligence Research and Development},
	year = {2023},
	pages = {81-93},
	publisher = {Springer},
	author = {Sharma, Akriti and Stensen, Mette and Delbarre, Erwan and Siddiqui, Momin and Haugen, Trine and Riegler, Michael and Hugo Lewi {Hammer}}
}
@article {43296,
	title = {Enhancing Questioning Skills through Child Avatar Chatbot Training with Feedback},
	journal = {Frontiers in Psychology},
	year = {2023},
	publisher = {Frontiers},
	doi = {10.3389/fpsyg.2023.1198235},
	author = {Ragnhild K. {R{\o}ed} and Gunn Astrid {Baugerud} and Syed Zohaib {Hassan} and Saeed Shafiee {Sabet} and Salehi, Pegah and Powell, Martine and Riegler, Michael and Halvorsen, P{\r a}l and Miriam Sinkerud {Johnson}}
}
@inproceedings {43282,
	title = {Generation of Synthetic Tabular Healthcare Data Using Generative Adversarial Networks},
	journal = {International conference on multimedia modeling},
	year = {2023},
	publisher = {Springer International Publishing},
	abstract = {High-quality tabular data is a crucial requirement for developing data-driven applications, especially healthcare-related ones, because most of the data nowadays collected in this context is in tabular form. However, strict data protection laws complicates the access to medical datasets. Thus, synthetic data has become an ideal alternative for data scientists and healthcare professionals to circumvent such hurdles. Although many healthcare institutions still use the classical de-identification and anonymization techniques for generating synthetic data, deep learning-based generative models such as generative adversarial networks (GANs) have shown a remarkable performance in generating tabular datasets with complex structures. This paper examines the GANs{\textquoteright} potential and applicability within the healthcare industry, which often faces serious challenges with insufficient training data and patient records sensitivity. We investigate several state-of-the-art GAN-based models proposed for tabular synthetic data generation. Healthcare datasets with different sizes, numbers of variables, column data types, feature distributions, and inter-variable correlations are examined. Moreover, a comprehensive evaluation framework is defined to evaluate the quality of the synthetic records and the viability of each model in preserving the patients{\textquoteright} privacy. The results indicate that the proposed models can generate synthetic datasets that maintain the statistical characteristics, model compatibility and privacy of the original data. Moreover, synthetic tabular healthcare datasets can be a viable option in many data-driven applications. However, there is still room for further improvements in designing a perfect architecture for generating synthetic tabular data.},
	keywords = {deep learning, Medical data, synthetic data generation},
	doi = {10.1007/978-3-031-27077-2_34},
	author = {Alireza Hossein Za {Nik} and Riegler, Michael and Halvorsen, P{\r a}l and Stor{\r a}s, Andrea}
}
@inproceedings {43256,
	title = {Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence},
	journal = {IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023)},
	year = {2023},
	abstract = {Meibomian gland dysfunction is the most common cause of dry eye disease, which is a prevalent condition that can damage the ocular surface and cause reduced vision and substantial pain. Meibum secreted from the meibomian glands makes up the majority of the outer, protective lipid layer of the tear film. Changes in the secreted meibum and markers of glandular damage can be detected through tear sampling.Several studies have investigated the tear film protein expression in meibomian gland dysfunction, but less work apply machine learning to analyze the protein patterns.\&nbsp;We use machine learning and methods from explainable artificial intelligence to detect potential clinically relevant proteins in meibomian gland dysfunction. Two different explainable artificial intelligence methods are compared. Several of the proteins found important in the models have been linked to dry eye disease in the past, while some are novel. Consequently, explainable artificial intelligence methods serve as a promising tool for screening for proteins that are relevant for meibomian gland dysfunction. By doing so, one may be able to discover new biomarkers and treatments, and gain a better understanding of how diseases develop.},
	keywords = {Dry eye disease, Explainable artificial intelligence, Machine learning, meibomian gland dysfunction, proteomics},
	author = {Stor{\r a}s, Andrea and Magn{\o}, Morten and Fineide, Fredrik and Thiede, Bernd and Chen, Xiangjun and Str{\"u}mke, Inga and Halvorsen, P{\r a}l and Utheim, Tor and Riegler, Michael}
}
@article {42995,
	title = {Live Streaming Technology and Online Child Sexual Exploitation and Abuse - A Scoping Review},
	journal = {Trauma, Violence, \& Abuse},
	year = {2023},
	publisher = { SAGE Publications},
	abstract = {Livestreaming of child sexual abuse is an established form of online child sexual exploitationand abuse. However, only a limited body of research has examined this issue. The Covid-19pandemic has accelerated internet use and user knowledge of livestreaming servicesemphasising the importance of understanding this crime. In this scoping review, existingliterature was brought together through an iterative search of eight databases containing peer-reviewed journal articles, as well as grey literature. Records were eligible for inclusion if theprimary focus was on livestream technology and online child sexual exploitation and abuse,the child being defined as eighteen years or younger. Fourteen of the 2,218 records wereselected. The data were charted and divided into four categories: victims, offenders,legislation, and technology. Limited research, differences in terminology, study design, andpopulation inclusion criteria present a challenge to drawing general conclusions on thecurrent state of livestreaming of child sexual abuse. The records show that victims arepredominantly female. The average livestream offender was found to be older than theaverage online child sexual abuse offender. Therefore, it is unclear whether the findings arerepresentative of the global population of livestream offenders. Furthermore, there appears tobe a gap in what the records show on platforms and payment services used and current digitaltrends. The lack of a legal definition and privacy considerations pose a challenge toinvestigation, detection, and prosecution. The available data allow some insights into apotentially much larger issue.},
	author = {Catharina Sofie Rode {Drejer} and Riegler, Michael and Miriam Sinkerud {Johnson} and Halvorsen, P{\r a}l and Gunn Astrid {Baugerud}}
}
@inproceedings {43336,
	title = {Man vs. AI: An in silico study of polyp detection performance},
	journal = {2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)},
	year = {2023},
	publisher = {IEEE},
	address = {L{\textquoteright}Aquila, Italy},
	abstract = {AI-based colon polyp detection systems have received much attention, and several products and prototypes report good results. In silico verification is a crucial step when developing such systems, but very few compare human versus AI performance. This paper, therefore, describes methods and results for an in silico test of an AI model with two different versions for polyp detection in colonoscopy and compares them to the performance of endoscopist doctors who reviewed the same colonoscopy video clips. The two versions have different thresholds for false positive rate reduction. Our models perform polyp detection within the range of the endoscopists{\textquoteright} performance, although faster, showing a potential for use in a clinical setting. For the AI and the endoscopists alike, the results show a trade-off between high sensitivity and high specificity; to achieve perfect detection, one will also get abundance of false positives. This can cause alarm fatigue in a clinical setting.\&nbsp;},
	doi = {10.1109/CBMS58004.2023.00307},
	url = {https://ieeexplore.ieee.org/document/10178833},
	author = {Pia H. {Smedsrud} and Espeland, H{\r a}vard and Tor Jan Derek {Berstad} and Petlund, Andreas and de Lange, Thomas and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {ali2021polypgen,
	title = {A multi-center polyp detection and segmentation dataset for generalisability assessment},
	journal = {Nature Scientific Data},
	volume = {10},
	year = {2023},
	publisher = {Nature},
	doi = {https://doi.org/10.1038/s41597-023-01981-y},
	url = {https://doi.org/10.1038/s41597-023-01981-y},
	author = {Ali, Sharib and Jha, Debesh and Ghatwary, Noha and Realdon, Stefano and Cannizzaro, Renato and Osama E {Salem} and Lamarque, Dominique and Daul, Christian and Kim V {Anonsen} and Riegler, Michael and Kim V. {{\r A}nonsen} and Petlund, Andreas and Halvorsen, P{\r a}l and Rittscher, Jens and de Lange, Thomas and James E. {East}}
}
@inproceedings {43077,
	title = {Multimedia datasets: challenges and future possibilities},
	journal = {International conference on multimedia modeling},
	year = {2023},
	doi = {https://doi.org/10.1007/978-3-031-27818-1_58},
	author = {Nguyen, Thu and Stor{\r a}s, Andrea and Thambawita, Vajira and Hicks, Steven and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {43319,
	title = {Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence},
	journal = {IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023)},
	year = {2023},
	abstract = {Dry eye disease is a common and potentially debilitating medical condition. Meibum secreted from the meibomian glands is the largest contributor to the outermost, protective lipid layer of the tear film. Dysfunction of the meibomian glands is the most common cause of dry eye disease. As meibomian gland dysfunction progresses, gradual atrophy of the glands is observed. The meibomian glands are commonly visualized through meibography, a technique requiring specialist equipment and knowledge that might not be available to the physician. In the present project we use machine learning on clinical tabular data to predict the degree of meibomian gland dropout. Moreover, we employ explainable artificial intelligence on the best performing algorithms for feature importance evaluation. The best performing algorithms were AdaBoost, multilayer perceptron\&nbsp;and LightGBM\&nbsp;which outperformed the majority vote baseline classifier in every included evaluation metric for both multioutput and binary classification. Through explainable artificial intelligence known associations are validated and novel connections identified and discussed.},
	keywords = {Dry eye disease, Explainable artificial intelligence, Machine learning, meibography, meibomian gland dysfunction},
	author = {Fineide, Fredrik and Stor{\r a}s, Andrea and Riegler, Michael and Utheim, Tor}
}
@inproceedings {43320,
	title = {Predicting the degree of meibomian gland dropout with artificial intelligence},
	journal = {ARVO Annual Meeting},
	year = {2023},
	author = {Fineide, Fredrik and Stor{\r a}s, Andrea and Magn{\o}, Morten and Hugo Lewi {Hammer} and Chen, Xiangjun and Khan, Ayyad and Halvorsen, P{\r a}l and Utheim, Tor and Riegler, Michael}
}
@inproceedings {43291,
	title = {ScopeSense: An 8.5-Month Sport, Nutrition, and Lifestyle Lifelogging Dataset},
	journal = {International Conference on Multimedia Modeling (MMM)},
	volume = {13833},
	year = {2023},
	pages = {502 - 514},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {Nowadays, most people have a smartphone that can track their everyday activities. Furthermore, a significant number of people wear advanced smartwatches to track several vital biomarkers in addition to activity data. However, it is still unclear how these data can actually be used to improve certain aspects of people{\textquoteright}s lives. One of the key challenges is that the collected data is often massive and unstructured. Therefore, a link to other important information (e.g., when, what, and how much food was consumed) is required. It is widely believed that such detailed and structured longitudinal data about a person is essential to model and provide personalized and precise guidance. Despite the strong belief of researchers about the power of such a data-driven approach, respective datasets have been difficult to collect. In this study, we present a unique dataset from two individuals performing a structured data collection over eight and a half months. In addition to the sensor data, we collected their nutrition, training, and well-being data. The availability of nutrition data with many other important objectives and subjective longitudinal data streams may facilitate research related to food for a healthy lifestyle. Thus, we present a sport, nutrition, and lifestyle logging dataset called\&nbsp;ScopeSense\&nbsp;from two individuals and discuss its potential use. The dataset is fully open for researchers, and we consider this study as a potential starting point for developing methods to collect and create knowledge for a larger cohort of people.},
	isbn = {978-3-031-27076-5},
	issn = {0302-9743},
	doi = {10.1007/978-3-031-27077-210.1007/978-3-031-27077-2_39},
	url = {https://link.springer.com/content/pdf/10.1007/978-3-031-27077-2https://link.springer.com/content/pdf/10.1007/978-3-031-27077-2.pdfhttps://link.springer.com/10.1007/978-3-031-27077-2_39https://link.springer.com/content/pdf/10.1007/978-3-031-27077-2_39},
	author = {Riegler, Michael and Thambawita, Vajira and Chatterjee, Ayan and Nguyen, Thu and Hicks, Steven and Telle-Hansen, Vibeke and Svein Arne {Pettersen} and Johansen, Dag and Jain, Ramesh and Halvorsen, P{\r a}l},
	editor = {Dang-Nguyen, Duc-Tien and Gurrin, Cathal and Larson, Martha and Alan F. {Smeaton} and Rudinac, Stevan and Dao, Minh-Son and Trattner, Christoph and Chen, Phoebe}
}
@article {43053,
	title = {Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs},
	journal = {Algorithms},
	year = {2023},
	month = {Jan-01-2023},
	pages = {30},
	publisher = {MDPI},
	doi = {10.3390/a16010030},
	url = {https://www.mdpi.com/1999-4893/16/1/30},
	author = {Nguyen, Phuc-Thinh and Dao, Minh-Son and Riegler, Michael and Rage Uday {Kiran} and Dang, Thai-Thinh and Le, Duy-Dong and Nguyen-Ly, Kieu-Chinh and Pham, Thanh-Qui and Nguyen, Van-Luong}
}
@article {43321,
	title = {Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis},
	journal = {Diagnostics},
	volume = {13},
	number = {2345},
	year = {2023},
	month = {07/2023},
	publisher = {MDPI},
	abstract = {Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way of increasing the understanding of {\textquotedblleft}black box{\textquotedblright} models and building trust. In this work, we applied transfer learning to develop a deep neural network to predict sex from electrocardiograms. Using the visual explanation method Grad-CAM, heat maps were generated from the model in order to understand how it makes predictions. To evaluate the usefulness of the heat maps and determine if the heat maps identified electrocardiogram features that could be recognized to discriminate sex, medical doctors provided feedback. Based on the feedback, we concluded that, in our setting, this mode of explainable artificial intelligence does not provide meaningful information to medical doctors and is not useful in the clinic. Our results indicate that improved explanation techniques that are tailored to medical data should be developed before deep neural networks can be applied in the clinic for diagnostic purposes.},
	keywords = {electrocardiograms, Explainable artificial intelligence, heat maps},
	doi = {https://doi.org/10.3390/diagnostics13142345},
	author = {Stor{\r a}s, Andrea and Ole Emil {Andersen} and Lockhart, Sam and Thielemann, Roman and Gnesin, Filip and Thambawita, Vajira and Hicks, Steven and Kanters, J{\o}rgen and Str{\"u}mke, Inga and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {43255,
	title = {Using explainable artificial intelligence (XAI) to explore factors affecting meibomian gland (MG) dropout},
	journal = {ARVO Annual Meeting},
	year = {2023},
	author = {Stor{\r a}s, Andrea and Fineide, Fredrik and Hugo Lewi {Hammer} and Khan, Ayyad and Magn{\o}, Morten and Xiangjung, Chen and Halvorsen, P{\r a}l and Utheim, Tor and Riegler, Michael}
}
@misc {42446,
	title = {ACM Multimedia Grand Challenge on Detecting Cheapfakes},
	year = {2022},
	note = {https://2022.acmmm.org/call-for-grand-challenge-submissions/},
	publisher = {ACM},
	address = {ACM Multimedia Conference (MM)},
	url = {https://detecting-cheapfakes.github.io},
	author = {Aneja, Shivangi and Midoglu, Cise and Dang-Nguyen, Duc-Tien and Khan, Sohail and Riegler, Michael and Halvorsen, P{\r a}l and Bregler, Chris and Adsumilli, Balu}
}
@article {42768,
	title = {{\'A}ika: A Distributed Edge System for AI Inference},
	journal = {Big Data and Cognitive Computing},
	volume = {6},
	year = {2022},
	month = {Jan-06-2022},
	pages = {68},
	publisher = {MDPI},
	abstract = {Video monitoring and surveillance of commercial fisheries in world oceans has been proposed by the governing bodies of several nations as a response to crimes such as overfishing. Traditional video monitoring systems may not be suitable due to limitations in the offshore fishing environment, including low bandwidth, unstable satellite network connections and issues of preserving the privacy of crew members. In this paper, we present {\'A}ika, a robust system for executing distributed Artificial Intelligence (AI) applications on the edge. {\'A}ika provides engineers and researchers with several building blocks in the form of Agents, which enable the expression of computation pipelines and distributed applications with robustness and privacy guarantees. Agents are continuously monitored by dedicated monitoring nodes, and provide applications with a distributed checkpointing and replication scheme. {\'A}ika is designed for monitoring and surveillance in privacy-sensitive and unstable offshore environments, where flexible access policies at the storage level can provide privacy guarantees for data transfer and access.\&nbsp;},
	doi = {10.3390/bdcc6020068},
	url = {https://www.mdpi.com/2504-2289/6/2/68},
	author = {Joakim Aalstad {Alslie} and Aril Bernhard {Ovesen} and Tor-Arne Schmidt {Nordmo} and H{\r a}vard Dagenborg {Johansen} and Halvorsen, P{\r a}l and Riegler, Michael and Johansen, Dag}
}
@article {38025,
	title = {Artificial Intelligence for Colonoscopy: Past, Present, and Future},
	journal = {IEEE Journal of Biomedical and Health Informatics},
	volume = {26},
	year = {2022},
	month = {Jan-01-2022},
	pages = {3950-3965},
	publisher = {IEEE},
	issn = {2168-2194},
	doi = {10.1109/JBHI.2022.3160098},
	url = {https://ieeexplore.ieee.org/document/9739863/http://xplorestaging.ieee.org/ielx7/6221020/6363502/09739863.pdf?arnumber=9739863},
	author = {Tavanapong, Wallapak and Oh, Junghwan and Riegler, Michael and Mohammed I {Khaleel} and Mitta, Bhuvan and Piet C. {De Groen}}
}
@article {28224,
	title = {Artificial intelligence in dry eye disease},
	journal = {The Ocular Surface},
	volume = {23},
	year = {2022},
	month = {Jan-01-2022},
	pages = {74 - 86},
	publisher = {Elsevier},
	abstract = {Dry eye disease (DED) has a prevalence of between 5 and 50\%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term {\textquoteleft}AI{\textquoteright} is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of\&nbsp;interferometry, slit-lamp and\&nbsp;meibography\&nbsp;images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.},
	keywords = {artificial intelligence, Dry eye disease, Machine learning},
	issn = {15420124},
	doi = {10.1016/j.jtos.2021.11.004},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1542012421001324},
	author = {Stor{\r a}s, Andrea and Str{\"u}mke, Inga and Riegler, Michael and Grauslund, Jakob and Hugo Lewi {Hammer} and Yazidi, Anis and Halvorsen, P{\r a}l and Kjell G. {Gundersen} and Tor P. {Utheim} and Catherine J. {Jackson}}
}
@inproceedings {42447,
	title = {Automatic Thumbnail Selection for Soccer Videos using Machine Learning},
	journal = {Proceedings of the 13th ACM Multimedia Systems Conference (MMSys {\textquoteright}22)},
	year = {2022},
	pages = {73-85},
	publisher = {ACM},
	doi = {10.1145/3524273.3528182},
	author = {Husa, Andreas and Midoglu, Cise and Hammou, Malek and Hicks, Steven and Johansen, Dag and Kupka, Tomas and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {42723,
	title = {Automatic Tracking of the ICSI procedure using Deep Learning},
	journal = {Human Reproduction},
	volume = {37},
	year = {2022},
	month = {07/2022},
	publisher = {Oxford University Press},
	abstract = {Study questionCan deep learning be used to detect and track spermatozoa and the different parts of an ICSI procedure?Summary answerDeep learning can be used as a tool to assist and organize the contents of an ICSI procedure.What is known alreadySperm tracking has been a topic of research and practice for many years, especially in the context of computer-aided sperm analysis (CASA). Recent studies have proposed using deep learning algorithms to track spermatozoa for spermatozoon selection in human and animal samples. One critical part of performing ICSI involves the selection of the {\textquotedblleft}best{\textquotedblright} spermatozoon for injection, but other parts of the procedure may also be of importance. However, as far as we know, tracking using deep learning has not been applied to the ICSI procedure, where detecting instruments and the oocyte could also be helpful in post-analysis and training.Study design, size, durationThe study was performed using three anonymized videos of the ICSI procedure. The frames of the videos were manually annotated by data scientists and verified by an embryologist. The annotations were bounding boxes around specific parts of the ICSI procedure, including sperm, pipettes, and the oocyte. We trained a YOLOv5 model on the collected data, where two videos were used for training and one video for validation.Participants/materials, setting, methodsThe videos of the ICSI procedure were captured at 200x magnification with a DeltaPix camera at Fertilitetssenteret in Oslo, Norway. ICSI was performed using a Nikon ECLIPSE TE2000-S microscope connected with Eppendorf TransferMan 4m micromanipulators. The spermatozoa were immobilised in 5 {\textmu}l Polyvinylpyrrolidone (PVP; CooperSurgical). The videos had a resolution of 1920x1080 and were resized to 640x640 before being processed by the YOLOv5 model. The data will be made public in a later study.Main results and the role of chanceMean average precision (mAP) with the threshold of 0.5 (mAP@.5) is the main quantitative parameter measured in the YOLOv5 model. All the experiments were performed using three-fold cross-validation, where we present the average metrics calculated over the three folds. Overall, the method showed an average mAP@.5 of 0.50 across all predicted classes, which means that the method can track the different components with good accuracy. Looking closer at the individual classes, we see that instruments like the holding pipette and ICSI pipette are detected with high accuracy with a mAP@.5 of 0.87 and 0.94, respectively. The oocyte is also easily tracked with a mAP@.5 of 0.92. The first polar body is well detected with a mAP@.5 of 0.65. The model has issues detecting and tracking individual sperm (both outside and within the pipette), where the method achieved a mAP@.5 of 0.46 for tracking sperm outside the pipette and 0.03 for the sperm inside the pipette. The low score of detecting the sperm in the pipette can be explained by the often unclear visibility of the sperm through the pipette and the low number of training samples.Limitations, reasons for cautionThe limited sample size makes the generalizability of the method difficult to determine. A more extensive evaluation is necessary. Moreover, as the currency study focuses on tracking, patient information and clinical outcome were not included in the analysis.Wider implications of the findingsDeep learning has the potential to aid embryologists to perform successful ICSI through tracking and detection of spermatozoa, pipettes, and the oocyte. This could potentially lead to better internal quality control and teaching possibilities, and hopefully better results.Trial registration numbernot applicable},
	issn = {0268-1161},
	doi = {10.1093/humrep/deac107.261},
	url = {https://academic.oup.com/humrep/article/37/Supplement_1/deac107.261/6619904},
	author = {Hicks, Steven and Thambawita, Vajira and Stor{\r a}s, Andrea and Trine B. {Haugen} and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Riegler, Michael and Mette Haug {Stensen}}
}
@inproceedings {42538,
	title = {Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure},
	journal = {NAIS: Symposium of the Norwegian AI Society },
	volume = {1650},
	year = {2022},
	pages = {1-11},
	publisher = {NAIS 2022},
	abstract = {The\&nbsp;in vitro\&nbsp;fertilization procedure called intracytoplasmic sperm injection can be used to help fertilize an egg by injecting a single sperm cell directly into the cytoplasm of the egg. In order to evaluate, refine and improve the method in the fertility clinic, the procedure is usually observed at the clinic. Alternatively, a video of the procedure can be examined and labeled in a time-consuming process. To reduce the time required for the assessment, we propose an unsupervised method that automatically clusters video frames of the intracytoplasmic sperm injection procedure. Deep features are extracted from the video frames and form the basis for a clustering method. The method provides meaningful clusters representing different stages of the intracytoplasmic sperm injection procedure. The clusters can lead to more efficient examinations and possible new insights that can improve clinical practice. Further on, it may also contribute to improved clinical outcomes due to increased understanding about the technical aspects and better results of the procedure. Despite promising results, the proposed method can be further improved by increasing the amount of data and exploring other types of features.},
	keywords = {clustering, Computer Vision and Pattern Recognition (cs.CV), human reproduction, medical videos, Unsupervised learning},
	isbn = {978-3-031-17029-4},
	doi = {10.1007/978-3-031-17030-0_9},
	author = {Stor{\r a}s, Andrea and Riegler, Michael and Trine B. {Haugen} and Thambawita, Vajira and Hicks, Steven and Hugo Lewi {Hammer} and Kakulavarapu, Radhika and Halvorsen, P{\r a}l and Stensen, Mette}
}
@inproceedings {sharma2022automating,
	title = {Automating tracking of cell division for human embryo development in time lapse videos},
	journal = {Human Reproduction},
	volume = {37},
	year = {2022},
	pages = {I305{\textendash}I306},
	publisher = {Oxford University Press},
	author = {Sharma, A and Kakulavarapu, R and Thambawita, Vajira and Siddiqui, M and Delbarre, E and Riegler, Michael and Hugo Lewi {Hammer} and Stensen, M}
}
@article {42998,
	title = {Automating tracking of cell division for human embryo development in time lapse videos},
	journal = {Human Reproduction},
	volume = {37},
	year = {2022},
	month = {Jun-06-2024},
	publisher = {Oxford University Press},
	issn = {0268-1161},
	doi = {10.1093/humrep/deac107.233},
	url = {https://academic.oup.com/humrep/article-pdf/37/Supplement_1/deac107.233/44306957/deac107.233.pdf},
	author = {Sharma, Akriti and Kakulavarapu, R and Thambawita, Vajira and Siddiqui, M and Delbarre, E and Riegler, Michael and Hugo Lewi {Hammer} and Stensen, M}
}
@article {42497,
	title = {Cell exclusion during human embryo development result in altered morphokinetic patterns up to morula formation},
	journal = {Human Reproduction},
	year = {2022},
	publisher = {Human Reproduction},
	author = {Radhika, Kakulavarapu and Erwan, Delbarre and Mette, Stensen and Haugen B. {Trine} and Riegler, Michael}
}
@misc {https://doi.org/10.48550/arxiv.2104.05642,
	title = {Common Limitations of Image Processing Metrics: A Picture Story},
	year = {2022},
	publisher = {arXiv},
	abstract = {While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide.},
	keywords = {Computer Vision and Pattern Recognition (cs.CV), electronic engineering, FOS: Computer and information sciences, FOS: Electrical engineering, Image and Video Processing (eess.IV), information engineering},
	doi = {10.48550/ARXIV.2104.05642},
	url = {https://arxiv.org/abs/2104.05642},
	author = {Reinke, Annika and Minu D. {Tizabi} and Carole H. {Sudre} and Eisenmann, Matthias and R{\"a}dsch, Tim and Baumgartner, Michael and Acion, Laura and Antonelli, Michela and Arbel, Tal and Bakas, Spyridon and Bankhead, Peter and Benis, Arriel and Cardoso, Jorge and Cheplygina, Veronika and Cimini, Beth and Gary S. {Collins} and Farahani, Keyvan and Glocker, Ben and Godau, Patrick and Hamprecht, Fred and Daniel A. {Hashimoto} and Heckmann-N{\"o}tzel, Doreen and Michael M. {Hoffmann} and Huisman, Merel and Isensee, Fabian and Jannin, Pierre and Charles E. {Kahn} and Karargyris, Alexandros and Karthikesalingam, Alan and Kainz, Bernhard and Kavur, Emre and Kenngott, Hannes and Kleesiek, Jens and Kooi, Thijs and Kozubek, Michal and Kreshuk, Anna and Kurc, Tahsin and Bennett A. {Landman} and Litjens, Geert and Madani, Amin and Maier-Hein, Klaus and Anne L. {Martel} and Mattson, Peter and Meijering, Erik and Menze, Bjoern and Moher, David and Karel G. M. {Moons} and M{\"u}ller, Henning and Nickel, Felix and Petersen, Jens and Polat, Gorkem and Rajpoot, Nasir and Reyes, Mauricio and Rieke, Nicola and Riegler, Michael and Rivaz, Hassan and Saez-Rodriguez, Julio and Clarisa Sanchez {Gutierrez} and Schroeter, Julien and Saha, Anindo and Shetty, Shravya and Stieltjes, Bram and Ronald M. {Summers} and Abdel A. {Taha} and Sotirios A. {Tsaftaris} and van Ginneken, Bram and Varoquaux, Ga{\"e}l and Wiesenfarth, Manuel and Ziv R. {Yaniv} and Kopp-Schneider, Annette and J{\"a}ger, Paul and Maier-Hein, Lena}
}
@inproceedings {43064,
	title = {A Comparative Study of Interactive Environments for Investigative Interview of A Virtual Child Avatar},
	journal = {IEEE international symposium on multimedia (ISM)},
	year = {2022},
	pages = {194-201},
	publisher = {IEEE},
	doi = {10.1109/ISM55400.2022.00043},
	author = {Syed Zohaib {Hassan} and Saeed Shafiee {Sabet} and Salehi, Pegah and Ko, Hayley and Riiser, Ingvild and Miriam Sinkerud {Johnson} and Gunn Astrid {Baugerud} and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {42709,
	title = {Comparison of Crowdsourced and Remote Subjective User Studies: A Case Study of Investigative Child Interviews},
	journal = {The 14th International Conference on Quality of Multimedia Experience},
	year = {2022},
	publisher = {IEEE},
	doi = {10.1109/QoMEX55416.2022.9900900},
	url = {https://ieeexplore.ieee.org/document/9900900},
	author = {Saeed Shafiee {Sabet} and Midoglu, Cise and Syed Zohaib {Hassan} and Salehi, Pegah and Riegler, Michael and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@article {28328,
	title = {Complexity and Variability Analyses of Motor Activity Distinguish Mood States in Bipolar Disorder},
	journal = {PLOS ONE},
	volume = {17},
	number = {1},
	year = {2022},
	pages = {e0262232},
	publisher = {PLOS ONE},
	author = {Jakobsen, Petter and Stautland, Andrea and Riegler, Michael and C{\^o}t{\'e}-Allard, Ulysse and Sepasdar, Zahra and Nordgreen, Tine and Torresen, Jim and Ole Bernt {Fasmer} and Ketil J. {Oedegaard}}
}
@misc {42555,
	title = {Data samlet fra {\r a}pne kilder er ikke ufarlig},
	year = {2022},
	publisher = {Aftenposten},
	address = {Kronikk},
	author = {Lysne, Olav and Str{\"u}mke, Inga and Riegler, Michael}
}
@article {42438,
	title = {Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources},
	journal = {Sensors},
	volume = {22},
	year = {2022},
	month = {01-04-2022},
	pages = {2802},
	publisher = {MPDI},
	doi = {10.3390/s22072802},
	url = {https://www.mdpi.com/1424-8220/22/7/2802https://www.mdpi.com/1424-8220/22/7/2802/pdf},
	author = {Siri S. {Eide} and Riegler, Michael and Hugo Lewi {Hammer} and John Bj{\o}rnar {Bremnes}}
}
@article {hammer2022efficient,
	title = {Efficient quantile tracking using an oracle},
	journal = {Applied Intelligence},
	volume = {53},
	year = {2022},
	pages = {289{\textendash}300},
	publisher = {Springer},
	author = {Hugo Lewi {Hammer} and Yazidi, Anis and Riegler, Michael and Rue, H{\r a}vard}
}
@inproceedings {42725,
	title = {Estimating Predictive Uncertainty in Gastrointestinal Polyp Segmentation},
	journal = {2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)},
	year = {2022},
	pages = {44-49},
	publisher = {IEEE},
	address = {Shenzen, China},
	abstract = {Deep neural networks have achieved state-of-the-art performance on numerous applications in the medical field, with use-cases ranging from automation of mundane tasks to diagnosis of life-threatening diseases. Despite these achievements, deep neural networks are considered {\textquotedblleft}black boxes{\textquotedblright} due to their complex structure and general lack of transparency in their decision-making process. These attributes make it challenging to incorporate deep learning into existing clinical workflows as decisions often need more support than blind faith in a statistical model. This paper presents an investigation of uncertainty estimation for the detection of colon polyps using deep convolutional neural networks (CNNs). We experiment with two different approaches to measure uncertainty, Monte Carlo (MC) dropout and deep ensembles, and discuss the advantages and disadvantages of both methods in terms of computational efficiency and performance gain. Furthermore, we apply the two uncertainty methods to two different state-of-the-art CNN-based polyp segmentation architectures. The uncertainty is visualized as heatmaps on the input images and can be used to make more informed decisions on whether or not to trust a model{\textquoteright}s predictions. The results show that the predictive uncertainties provide a comparison between different models{\textquoteright} predictions which can be interpreted as contrastive explanations where the values are largely influenced by the degree of independence between the models in the ensemble. We also reveal that MC dropout is shown to lack at providing contrastive uncertainty values due to the high correlation between the models{\textquoteright} in the ensemble.},
	doi = {10.1109/CBMS55023.2022.00015},
	url = {https://ieeexplore.ieee.org/document/9867012/http://xplorestaging.ieee.org/ielx7/9866947/9866899/09867012.pdf?arnumber=9867012},
	author = {Jacobsen, Felicia and Hicks, Steven and Halvorsen, P{\r a}l and Riegler, Michael}
}
@article {hicks2022evaluation,
	title = {On evaluation metrics for medical applications of artificial intelligence},
	journal = {Scientific Reports},
	volume = {12},
	number = {1},
	year = {2022},
	pages = {1{\textendash}9},
	publisher = {Nature Publishing Group},
	author = {Hicks, Steven and Str{\"u}mke, Inga and Thambawita, Vajira and Hammou, Malek and Riegler, Michael and Halvorsen, P{\r a}l and Parasa, Sravanthi}
}
@inproceedings {43138,
	title = {Experiences and Lessons Learned from a Crowdsourced-Remote Hybrid User Survey Framework},
	journal = {2022 IEEE International Symposium on Multimedia (ISM)},
	year = {2022},
	pages = {161-162},
	publisher = {IEEE},
	address = {Italy},
	abstract = {Subjective user studies are important to ensure the fidelity and usability of systems that generate multimedia content. Testing how end-users and domain experts perceive multimedia assets might provide crucial information. In this paper, we present our experiences with the open source hybrid crowdsourced-remote user survey framework called Huldra, which is intended for conducting web-based subjective user studies and aims to integrate the individual benefits associated with traditional, crowdsourced, and remote methods. We disseminate our experiences and insights from two actively deployed use cases and discuss challenges and opportunities associated with using Huldra as a framework for conducting user studies.},
	doi = {10.1109/ISM55400.2022.00035},
	url = {https://ieeexplore.ieee.org/document/10019678},
	author = {Midoglu, Cise and Stor{\r a}s, Andrea and Saeed Shafiee {Sabet} and Hammou, Malek and Hicks, Steven and Str{\"u}mke, Inga and Riegler, Michael and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@inproceedings {42464,
	title = {Explainability methods for machine learning systems for multimodal medical datasets: research proposal},
	journal = {ACM Multimedia Systems (MMSys) Conference},
	year = {2022},
	pages = {347-351},
	publisher = {ACM},
	abstract = {This paper contains the research proposal of Andrea M. Stor{\r a}s that was presented at the MMSys 2022 doctoral symposium. Machine learning models have the ability to solve medical tasks with a high level of performance, e.g., classifying medical videos and detecting anomalies using different sources of data. However, many of these models are highly complex and difficult to understand. Lack of interpretability can limit the use of machine learning systems in the medical domain. Explainable artificial intelligence provides explanations regarding the models and their predictions. In this PhD project, we develop machine learning models for automatic analysis of medical data and explain the results using established techniques from the field of explainable artificial intelligence. Current research indicate that there are still open issues to be solved in order for end users to understand multimedia systems powered by machine learning. Consequently, new explanation techniques will also be developed. Different types of medical data are applied in order to investigate the generalizability of the methods.},
	isbn = {978-1-4503-9283-9/22/06},
	doi = {10.1145/3524273.3533925},
	author = {Stor{\r a}s, Andrea and Str{\"u}mke, Inga and Riegler, Michael and Halvorsen, P{\r a}l}
}
@misc {42696,
	title = {Explainable Artificial Intelligence in Medicine},
	howpublished = {Nordic AI Meet 2022},
	year = {2022},
	abstract = {Machine learning (ML) has shown outstanding abilities to solve a large variety of tasks such as image recognition and natural language processing, which has huge relevance for the medical field. Complex ML models, including convolutional neural networks (CNNs), are used to analyse high dimensional data such as images and videos from medical examinations.\&nbsp;With increasing model complexity, the demand for techniques improving human understanding of the ML models also increases. If medical doctors do not understand how the models work, they might not know when the models are actually wrong or even refuse to use them. This can hamper the implementation of ML systems in the clinic and negatively affect patients.\&nbsp;To promote successful integration of ML systems in the clinic, it is important to provide explanations that establish trust in the models among healthcare personnel.\&nbsp;Explainable artificial intelligence (XAI) aims to provide explanations about ML models and their predictions. Several techniques have already been developed.\&nbsp;Existing XAI methods often fail to meet the requirements of medical doctors, probably because they are not sufficiently involved in the development of the methods.\&nbsp;We develop ML models solving tasks in various medical domains. The resulting models are explained using a selection of existing XAI methods, and the explanations are evaluated by medical experts. Their feedback is used to develop improved XAI methods.\&nbsp;We have investigated established techniques for making ML systems more transparent in the fields of gastroenterology, assisted reproductive technology, organ transplantation and cardiology.\&nbsp;Experiences from our projects will be used to develop new explanation techniques for clinical practice in close collaboration with medical experts.},
	keywords = {Explainable artificial intelligence, Machine learning, medicine},
	author = {Stor{\r a}s, Andrea and Str{\"u}mke, Inga and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {42761,
	title = {Exploration of Different Time Series Models for Soccer Athlete Performance Prediction},
	journal = {MDPI Engineering Proceedings},
	volume = {18},
	year = {2022},
	pages = {37},
	publisher = {MDPI},
	doi = {10.3390/engproc2022018037},
	author = {Kulakou, Siarhei and Ragab, Nourhan and Midoglu, Cise and Boeker, Matthias and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {39351,
	title = {FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	year = {2022},
	month = {Jan-01-2022},
	pages = {1 - 14},
	publisher = { IEEE},
	abstract = {The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Image segmentation, which is crucial for any quantitative analysis, has especially attracted attention. Recent hardware advancement has led to the success of deep learning approaches. However, although deep learning models are being trained on large datasets, existing methods do not use the information from different learning epochs effectively. In this work, we leverage the information of each training epoch to prune the prediction maps of the subsequent epochs. We propose a novel architecture called feedback attention network (FANet) that unifies the previous epoch mask with the feature map of the current training epoch. The previous epoch mask is then used to provide hard attention to the learned feature maps at different convolutional layers. The network also allows rectifying the predictions in an iterative fashion during the test time. We show that our proposed feedback attention model provides a substantial improvement on most segmentation metrics tested on seven publicly available biomedical imaging datasets demonstrating the effectiveness of FANet. The source code is available at https://github.com/nikhilroxtomar/FANet.},
	issn = {2162-237X},
	doi = {10.1109/TNNLS.2022.3159394},
	url = {https://ieeexplore.ieee.org/document/9741842},
	author = {Nikhil Kumar {Tomar} and Jha, Debesh and Riegler, Michael and H{\r a}vard D. {Johansen} and Johansen, Dag and Rittscher, Jens and Halvorsen, P{\r a}l and Ali, Sharib}
}
@article {nordmo2022fish,
	title = {Fish AI: Sustainable Commercial Fishing},
	journal = {Nordic Machine Intelligence},
	volume = {2},
	number = {2},
	year = {2022},
	pages = {1{\textendash}3},
	publisher = {NMI},
	address = {Oslo, Norway},
	doi = {https://doi.org/10.5617/nmi.9657},
	url = {https://journals.uio.no/NMI/article/view/9657},
	author = {Tor-Arne Schmidt {Nordmo} and Kvalsvik, Ove and Svein Ove {Kvalsund} and Hansen, Birte and Riegler, Michael}
}
@inproceedings {42441,
	title = {HOST-ATS: Automatic Thumbnail Selection with Dashboard-Controlled ML Pipeline and Dynamic User Survey},
	journal = {Proceedings of the 13th ACM Multimedia Systems Conference (MMSys {\textquoteright}22)},
	year = {2022},
	pages = {334-340},
	publisher = {ACM},
	doi = {10.1145/3524273.3532908},
	author = {Husa, Andreas and Midoglu, Cise and Hammou, Malek and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {42442,
	title = {Huldra: A Framework for Collecting Crowdsourced Feedback on Multimedia Assets},
	journal = {Proceedings of the 13th ACM Multimedia Systems Conference (MMSys {\textquoteright}22)},
	year = {2022},
	pages = {203-209},
	publisher = {ACM},
	doi = {10.1145/3524273.3532887},
	author = {Hammou, Malek and Midoglu, Cise and Hicks, Steven and Stor{\r a}s, Andrea and Saeed Shafiee {Sabet} and Str{\"u}mke, Inga and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {42711,
	title = {Human vs. GPT-3: The challenges of extracting emotions from child responses},
	journal = {The 14th International Conference on Quality of Multimedia Experience},
	year = {2022},
	publisher = { IEEE},
	isbn = {978-1-6654-8794-8},
	issn = {2472-7814},
	doi = {10.1109/QoMEX55416.2022.9900885},
	url = {https://ieeexplore.ieee.org/document/9900885},
	author = {Lammerse, Myrthe and Syed Zohaib {Hassan} and Saeed Shafiee {Sabet} and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {42546,
	title = {ICDAR{\textquoteright}22: Intelligent Cross-Data Analysis and Retrieval},
	journal = {2022 International Conference on Multimedia Retrieval},
	year = {2022},
	month = {06/2022},
	publisher = {ACM},
	address = {2022, Newark, NJ, USA},
	abstract = {We have witnessed the rise of cross-data against multimodal dataproblems recently. The cross-modal retrieval system uses a textualquery to look for images; the air quality index can be predictedusing lifelogging images; the congestion can be predicted usingweather and tweets data; daily exercises and meals can help topredict the sleeping quality are some examples of this researchdirection. Although vast investigations focusing on multimodal dataanalytics have been developed, few cross-data (e.g., cross-modaldata, cross-domain, cross-platform) research has been carried on.In order to promote intelligent cross-data analytics and retrievalresearch and to bring a smart, sustainable society to human beings,the specific article collection on "Intelligent Cross-Data Analysisand Retrieval" is introduced. This Research Topic welcomes thosewho come from diverse research domains and disciplines such aswell-being, disaster prevention and mitigation, mobility, climatechange, tourism, healthcare, and food computing.},
	author = {Dao, Minh-Son and Riegler, Michael and Dang-Nguyen, Duc-Tien and Gurrin, Cathal and Nakashima, Yuta and Dong, Mianxiong}
}
@inproceedings {28231,
	title = {Investigative Interviews using a Multimodal Virtual Avatar},
	journal = {American Psychology-Law Society Conference 2022},
	year = {2022},
	publisher = {American Psychology-Law Society},
	address = {Denver USA,},
	abstract = {To meet best-practice standards, we are developing an interactive virtual avatar aiming as a training tool to raise interviewing skills of child-welfare and law-enforcement professionals. Therefore, we present the {\textquotedblleft}Ilma{\textquotedblright} avatar that recognizes interviewers{\textquoteright} behavior during open-ended, closed and leading questions, and which can automatically respond to the conversation. We conducted a user study in which master students (N=3) and child protective workers (N=8)\&nbsp; interviewed {\textquotedblleft}Ilma{\textquotedblright}\&nbsp; and rated their perception of the interaction. The results show that the participants valued the interaction and found the avatar useful. Thus, it has great potential to be an effective training tool.},
	author = {Gunn Astrid {Baugerud} and Powell, Martine and Riegler, Michael and Halvorsen, P{\r a}l and Michael E. {Lamb} and Saeed Shafiee {Sabet} and Syed Zohaib {Hassan} and Salehi, Pegah and Ragnhild K. {R{\o}ed} and Miriam S. {Johnson}}
}
@inproceedings {42726,
	title = {LSTM Step Prediction and Ontology-Based Recommendation Generation in Activity eCoaching},
	journal = {2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)},
	year = {2022},
	month = {10/2022},
	pages = {13-18},
	publisher = {IEEE},
	address = {Greece},
	author = {Chatterjee, Ayan and Pahari, Nibedita and Riegler, Michael and Prinz, Andreas}
}
@article {42994,
	title = {Machine learning and ontology in eCoaching for personalized activity level monitoring and recommendation generation},
	journal = {Scientific Reports},
	volume = {12},
	year = {2022},
	month = {Jan-12-2022},
	pages = {19825},
	publisher = {Nature},
	doi = {10.1038/s41598-022-24118-4},
	url = {https://www.nature.com/articles/s41598-022-24118-4https://www.nature.com/articles/s41598-022-24118-4.pdfhttps://www.nature.com/articles/s41598-022-24118-4.pdfhttps://www.nature.com/articles/s41598-022-24118-4},
	author = {Chatterjee, Ayan and Pahari, Nibedita and Prinz, Andreas and Riegler, Michael}
}
@article {39342,
	title = {Meta-learning with implicit gradients in a few-shot setting for medical image segmentation},
	journal = {Computers in Biology and Medicine},
	volume = {143},
	year = {2022},
	month = {Jan-04-2022},
	pages = {105227},
	publisher = {Elsevier},
	abstract = {Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited for medical imaging for clinical practice. Using separately trained models for each unique lesion category or a unique patient population will require sufficiently large curated datasets, which is not practical to use in a real-world clinical set-up. Few-shot learning approaches can not only minimize the need for an enormous number of reliable ground truth labels that are labour-intensive and expensive, but can also be used to model on a dataset coming from a new population. To this end, we propose to exploit an optimization-based implicit model agnostic meta-learning (iMAML) algorithm under few-shot settings for medical image segmentation. Our approach can leverage the learned weights from diverse but small training samples to perform analysis on unseen datasets with high accuracy. We show that, unlike classical few-shot learning approaches, our method improves generalization capability. To our knowledge, this is the first work that exploits iMAML for medical image segmentation and explores the strength of the model on scenarios such as meta-training on unique and mixed instances of lesion datasets. Our quantitative results on publicly available skin and polyp datasets show that the proposed method outperforms the naive supervised baseline model and two recent few-shot segmentation approaches by large margins. In addition, our iMAML approach shows an improvement of 2\%{\textendash}4\% in dice score compared to its counterpart MAML for most experiments.},
	issn = {00104825},
	doi = {10.1016/j.compbiomed.2022.105227},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0010482522000191},
	author = {Khadka, Rabindra and Jha, Debesh and Hicks, Steven and Thambawita, Vajira and Riegler, Michael and Ali, Sharib and Halvorsen, P{\r a}l}
}
@inproceedings { reinke2022metrics,
	title = {Metrics Reloaded - A new recommendation framework for biomedical image analysis validation},
	journal = {Medical Imaging with Deep Learning},
	year = {2022},
	publisher = {MIDL 2022},
	abstract = {Meaningful performance assessment of biomedical image analysis algorithms depends on objective and appropriate performance metrics. There are major shortcomings in the current state of the art. Yet, so far limited attention has been paid to practical pitfalls associated when using particular metrics for image analysis tasks. Therefore, a number of international initiatives have collaborated to offer researchers with guidance and tools for selecting performance metrics in a problem-aware manner. In our proposed framework, the characteristics of the given biomedical problem are first captured in a problem fingerprint, which identifies properties related to domain interests, the target structure(s), the input datasets, and algorithm output. A problem category-specific mapping is applied in the second step to match fingerprints to metrics that reflect domain requirements. Based on input from experts from more than 60 institutions worldwide, we believe our metric recommendation framework to be useful to the MIDL community and to enhance the quality of biomedical image analysis algorithm validation.},
	url = {https://openreview.net/forum?id=24kBqy8rcB_},
	author = {Reinke, Annika and Christodoulou, Evangelia and Glocker, Ben and Scholz, Patrick and Isensee, Fabian and Kleesiek, Jens and Kozubek, Michal and Reyes, Mauricio and Riegler, Michael and Metrcis Matter {Consortium}}
}
@inproceedings {42708,
	title = {Is More Realistic Better? A Comparison of Game Engine and GAN-based Avatars for Investigative Interviews of Children},
	journal = {ICDAR {\textquoteright}22: Proceedings of the 3rd ACM Workshop on Intelligent Cross-Data Analysis and Retrieval},
	year = {2022},
	pages = {41-49},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {9781450392419},
	doi = {10.1145/351273110.1145/3512731.3534209},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3512731},
	author = {Salehi, Pegah and Syed Zohaib {Hassan} and Saeed Shafiee {Sabet} and Gunn Astrid {Baugerud} and Miriam Sinkerud {Johnson} and Halvorsen, P{\r a}l and Riegler, Michael},
	editor = {Dao, Minh-Son and Dang-Nguyen, Duc-Tien and Riegler, Michael}
}
@article {39305,
	title = {MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation},
	journal = {IEEE Journal of Biomedical and Health Informatics},
	volume = {26},
	year = {2022},
	month = {12/2021},
	pages = {2252-2263},
	publisher = {IEEE},
	abstract = {Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of variable sizes and train on small and biased datasets, which are common for biomedical use cases. While methods exist that incorporate multi-scale fusion approaches to address the challenges arising with variable sizes, they usually use complex models that are more suitable for general semantic segmentation problems. In this paper, we propose a novel architecture called Multi-Scale Residual Fusion Network (MSRF-Net), which is specially designed for medical image segmentation. The proposed MSRF-Net is able to exchange multi-scale features of varying receptive fields using a Dual-Scale Dense Fusion (DSDF) block. Our DSDF block can exchange information rigorously across two different resolution scales, and our MSRF sub-network uses multiple DSDF blocks in sequence to perform multi-scale fusion. This allows the preservation of resolution, improved information flow and propagation of both high- and low-level features to obtain accurate segmentation maps. The proposed MSRF-Net allows to capture object variabilities and provides improved results on different biomedical datasets. Extensive experiments on MSRF-Net demonstrate that the proposed method outperforms the cutting edge medical image segmentation methods on four publicly available datasets. We achieve the Dice Coefficient (DSC) of 0.9217, 0.9420, and 0.9224, 0.8824 on Kvasir-SEG, CVC-ClinicDB, 2018 Data Science Bowl dataset, and ISIC-2018 skin lesion segmentation challenge dataset respectively. We further conducted generalizability tests that also achieved the highest DSC score with 0.7921 and 0.7575 on CVC-ClinicDB and Kvasir-SEG, respectively.\&nbsp;},
	issn = {2168-2194},
	doi = {10.1109/JBHI.2021.3138024},
	url = {https://ieeexplore.ieee.org/document/9662196},
	author = {Srivastava, Abhishek and Jha, Debesh and Chanda, Sukalpa and Pal, Umapada and Johansen, Havard and Johansen, Dag and Riegler, Michael and Ali, Sharib and Halvorsen, P{\r a}l}
}
@inproceedings {42796,
	title = {Njord: a fishing trawler dataset},
	journal = {Proceedings of the 13th ACM Multimedia Systems Conference (MMSYS)},
	year = {2022},
	month = {08/2022},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {Fish is one of the main sources of food worldwide. The commercial fishing industry has a lot of different aspects to consider, ranging from sustainability to reporting. The complexity of the domain also attracts a lot of research from different fields like marine biology, fishery sciences, cybernetics, and computer science. In computer science, detection of fishing vessels via for example remote sensing and classification of fish from images or videos using machine learning or other analysis methods attracts growing attention. Surprisingly, little work has been done that considers what is happening on board the fishing vessels. On the deck of the boats, a lot of data and important information are generated with potential applications, such as automatic detection of accidents or automatic reporting of fish caught. This paper presents Njord, a fishing trawler dataset consisting of surveillance videos from a modern off-shore fishing trawler at sea. The main goal of this dataset is to show the potential and possibilities that analysis of such data can provide. In addition to the data, we provide a baseline analysis and discuss several possible research questions this dataset could help answer.},
	isbn = {9781450392839},
	doi = {10.1145/3524273.3532886},
	url = {https://dl.acm.org/doi/pdf/10.1145/3524273.3532886},
	author = {Tor-Arne Schmidt {Nordmo} and Aril Bernhard {Ovesen} and Bj{\o}rn Aslak {Juliussen} and Hicks, Steven and Thambawita, Vajira and H{\r a}vard Dagenborg {Johansen} and Halvorsen, P{\r a}l and Riegler, Michael and Johansen, Dag},
	editor = {Murray, Niall and Simon, Gwendal and Farias, Mylene and Viola, Irene and Montagud, Mario}
}
@inproceedings {nguyen2022parallel,
	title = {Parallel feature selection based on the trace ratio criterion},
	journal = {International Joint Conference on Neural Networks (IJCNN)},
	year = {2022},
	publisher = {IEEE},
	author = {Nguyen, Thu and Thanh Nhan {Phan} and Van Nhuong {Nguyen} and Thanh Binh {Nguyen} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {42722,
	title = {PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps},
	journal = {IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)},
	year = {2022},
	month = {07/2022},
	pages = {66-71},
	publisher = {IEEE},
	abstract = {Early identification of a polyp in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Developing computer-aided diagnosis (CAD) systems to detect polyps can improve detection accuracy and efficiency and save the time of the domain experts called endoscopists. Lack of annotated data is a common challenge when building CAD systems. Generating synthetic medical data is an active research area to overcome the problem of having relatively few true positive cases in the medical domain. To be able to efficiently train machine learning (ML) models, which are the core of CAD systems, a considerable amount of data should be used. In this respect, we propose the PolypConnect pipeline, which can convert non-polyp images into polyp images to increase the size of training datasets for training. We present the whole pipeline with quantitative and qualitative evaluations involving endoscopists. The polyp segmentation model trained using synthetic data, and real data shows a 5.1\% improvement of mean intersection over union (mIOU), compared to the model trained only using real data. The codes of all the experiments are available on GitHub to reproduce the results.},
	doi = {10.1109/CBMS55023.2022.00019},
	author = {Jan Andre {Fageren} and Thambawita, Vajira and Stor{\r a}s, Andrea and Parasa, Sravanthi and de Lange, Thomas and Halvorsen, P{\r a}l and Riegler, Michael}
}
@article {43095,
	title = {Predicting an unstable tear film through artificial intelligence},
	journal = {Scientific Reports},
	volume = {12},
	year = {2022},
	month = {12/2022},
	pages = {21416},
	publisher = {Springer Nature},
	abstract = {Dry eye disease is one of the most common ophthalmological complaints and is defined by a loss of tear film homeostasis. Establishing a diagnosis can be time-consuming, resource demanding and unpleasant for the patient. In this pilot study, we retrospectively included clinical data from 431 patients with dry eye disease examined in the Norwegian Dry Eye Clinic to evaluate how artificial intelligence algorithms perform on clinical data related to dry eye disease. The data was processed and subjected to numerous machine learning classification algorithms with the aim to predict decreased tear film break-up time. Moreover, feature selection techniques (information gain and information gain ratio) were applied to determine which clinical factors contribute most to an unstable tear film. The applied machine learning algorithms outperformed baseline classifications performed with\&nbsp;ZeroR\&nbsp;according to included evaluation metrics. Clinical features such as ocular surface staining, meibomian gland expressibility and dropout, blink frequency, osmolarity, meibum quality and symptom score were recognized as important predictors for tear film instability. We identify and discuss potential limitations and pitfalls.},
	doi = {10.1038/s41598-022-25821-y},
	url = {https://www.nature.com/articles/s41598-022-25821-y},
	author = {Fineide, Fredrik and Stor{\r a}s, Andrea and Chen, Xiangjun and Morten S. {Magn{\o}} and Yazidi, Anis and Riegler, Michael and Tor Paaske {Utheim}}
}
@misc {42540,
	title = {Predicting drug exposure in kidney transplanted patients using machine learning},
	year = {2022},
	type = {Poster presentation},
	address = {NORA Annual Conference, Stavanger, Norway},
	author = {Stor{\r a}s, Andrea and Riegler, Michael and Halvorsen, P{\r a}l and {\r A}sberg, Anders and Str{\"u}mke, Inga}
}
@inproceedings {42542,
	title = {Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning},
	journal = {35th IEEE CBMS International Symposium on Computer-Based Medical Systems},
	year = {2022},
	pages = {38-43},
	publisher = { IEEE},
	abstract = {Tacrolimus is one of the cornerstone immunosuppressive drugs in most transplantation centers worldwide following solid organ transplantation. Therapeutic drug monitoring of tacrolimus is necessary in order to avoid rejection of the transplanted organ or severe side effects. However, finding the right dose for a given patient is challenging, even for experienced clinicians. Consequently, a tool that can accurately estimate the drug exposure for individual dose adaptions would be of high clinical value. In this work, we propose a new technique using machine learning to estimate the tacrolimus exposure in kidney transplant recipients. Our models achieve predictive errors that are at the same level as an established population pharmacokinetic model, but are faster to develop and require less knowledge about the pharmacokinetic properties of the drug.},
	keywords = {Machine learning, personalized medicine, transplantation},
	doi = {10.1109/CBMS55023.2022.00014},
	author = {Stor{\r a}s, Andrea and {\r A}sberg, Anders and Halvorsen, P{\r a}l and Riegler, Michael and Str{\"u}mke, Inga}
}
@inproceedings {42502,
	title = {Prediction Modeling in Activity eCoaching for Tailored Recommendation Generation: A Conceptualization},
	journal = {International Symposium on Medical Measurements and Applications (MeMeA)},
	year = {2022},
	publisher = {IEEE},
	doi = {10.1109/MeMeA54994.2022.9856556},
	author = {Chatterjee, Ayan and Prinz, Andreas and Riegler, Michael}
}
@article {42713,
	title = {Prediction of Schizophrenia from Activity Data using Hidden Markov Model Parameters},
	journal = {Neural Computing and Applications},
	year = {2022},
	publisher = { Springer},
	author = {Boeker, Matthias and Riegler, Michael and Hugo Lewi {Hammer} and Jakobsen, Petter and Halvorsen, P{\r a}l}
}
@inproceedings {42480,
	title = {RCAD:Real-time Collaborative Anomaly Detection System for Mobile Broadband Networks},
	journal = {ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
	year = {2022},
	pages = {2682{\textendash}2691},
	publisher = {ACM},
	abstract = {The rapid increase in mobile data traffic and the number of connected devices and applications in networks is putting a significant pressure on the current network management approaches that heavily rely on human operators. Consequently, an automated network management system that can efficiently predict and detect anomalies is needed.\&nbsp; In this paper, we propose, RCAD, a novel distributed architecture for detecting anomalies in network data forwarding latency in an unsupervised fashion. RCAD employs the hierarchical temporal memory (HTM) algorithm for the online detection of anomalies.\&nbsp; It also involves a collaborative distributed learning module that facilitates knowledge sharing across the system.\&nbsp; We implement and evaluate RCAD on real world measurements from a commercial mobile network.\&nbsp; RCAD achieves over 0.7 F-1 score significantly outperforming the state-of-the-art methods.\&nbsp;},
	author = {Azza Hassan Moh {Ahmed} and Riegler, Michael and Hicks, Steven and Elmokashfi, Ahmed}
}
@article {42720,
	title = {Real-time deep learning based multi object tracking of spermatozoa in fresh samples},
	journal = {Human Reproduction},
	volume = {37},
	year = {2022},
	month = {06/2022},
	publisher = {Oxford University Press},
	doi = {https://doi.org/10.1093/humrep/deac107.104},
	author = {Thambawita, Vajira and Hicks, Steven and Stor{\r a}s, Andrea and Witczak, Oliwia and Jorunn M. {Andersen} and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Riegler, Michael and Haugen B. {Trine}}
}
@inproceedings {torpmann2022segmentation,
	title = {Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation},
	journal = {2022 IEEE International Symposium on Multimedia (ISM)},
	year = {2022},
	pages = {42-49},
	publisher = {IEEE},
	doi = {10.1109/ISM55400.2022.00012},
	author = {Torpmann-Hagen, Birk and Thambawita, Vajira and Glette, Kyrre and Halvorsen, P{\r a}l and Riegler, Michael}
}
@article {42539,
	title = {SinGAN-Seg: Synthetic training data generation for medical image segmentation},
	journal = {PLOS ONE},
	volume = {17},
	year = {2022},
	month = {05/2022},
	pages = {e0267976},
	publisher = {PLOS},
	abstract = {Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Therefore, artificial intelligence has become a popular tool for the automatic processing of medical data, acting as a supportive tool for doctors. However, the machine learning models used to build these tools are highly dependent on the data used to train them. Large amounts of data can be difficult to obtain in medicine due to privacy reasons, expensive and time-consuming annotations, and a general lack of data samples for infrequent lesions. In this study, we present a novel synthetic data generation pipeline, called\&nbsp;SinGAN-Seg, to produce synthetic medical images with corresponding masks using a single training image. Our method is different from the traditional generative adversarial networks (GANs) because our model needs only a single image and the corresponding ground truth to train. We also show that the synthetic data generation pipeline can be used to produce alternative artificial segmentation datasets with corresponding ground truth masks when real datasets are not allowed to share. The pipeline is evaluated using qualitative and quantitative comparisons between real data and synthetic data to show that the style transfer technique used in our pipeline significantly improves the quality of the generated data and our method is better than other state-of-the-art GANs to prepare synthetic images when the size of training datasets are limited. By training UNet++ using both real data and the synthetic data generated from the SinGAN-Seg pipeline, we show that the models trained on synthetic data have very close performances to those trained on real data when both datasets have a considerable amount of training data. In contrast, we show that synthetic data generated from the SinGAN-Seg pipeline improves the performance of segmentation models when training datasets do not have a considerable amount of data. All experiments were performed using an open dataset and the code is publicly available on GitHub.},
	doi = {https://doi.org/10.1371/journal.pone.0267976},
	url = {https://doi.org/10.1371/journal.pone.0267976},
	author = {Thambawita, Vajira and Salehi, Pegah and Sajad Amouei {Sheshkal} and Hicks, Steven and Hugo Lewi {Hammer} and Parasa, Sravanthi and de Lange, Thomas and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inbook {42536,
	title = {Smittestopp analytics: Analysis of position data},
	booktitle = { Smittestopp - A Case Study on Digital Contact Tracing},
	volume = {11},
	year = {2022},
	month = {06/2022},
	pages = {63 - 79},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Contact tracing applications generally rely on Bluetooth data. This type of data works well to determine whether a contact occurred (smartphones were close to each other) but cannot offer the contextual information GPS data can offer. Did the contact happen on a bus? In a building? And of which type? Are some places recurrent contact locations? By answering such questions, GPS data can help develop more accurate and better-informed contact tracing applications. This chapter describes the ideas and approaches implemented for GPS data within the Smittestopp contact tracing application.We will present the pipeline used and the contribution of GPS data for contextual information, using inferred transport modes and surrounding POIs, showcasing the opportunities in the use of GPS information. Finally,we discuss ethical and privacy considerations, as well as some lessons learned.},
	isbn = {978-3-031-05465-5},
	issn = {2512-1677},
	doi = {10.1007/978-3-031-05466-2_4},
	url = {https://link.springer.com/content/pdf/10.1007/978-3-031-05466-2_4},
	author = {Thambawita, Vajira and Hicks, Steven and Jaouen, Ewan and Halvorsen, P{\r a}l and Riegler, Michael},
	editor = {Elmokashfi, Ahmed and Lysne, Olav and Naumova, Valeriya}
}
@article {42991,
	title = {Synthesizing a Talking Child Avatar to Train Interviewers Working with Maltreated Children},
	journal = {Big Data and Cognitive Computing},
	volume = {6},
	year = {2022},
	month = {Jan-06-2022},
	pages = {62},
	publisher = {MDPI},
	doi = {10.3390/bdcc6020062},
	url = {https://www.mdpi.com/2504-2289/6/2/62https://www.mdpi.com/2504-2289/6/2/62/pdf},
	author = {Salehi, Pegah and Syed Zohaib {Hassan} and Lammerse, Myrthe and Saeed Shafiee {Sabet} and Riiser, Ingvild and Ragnhild Klingenber {R{\o}ed} and Miriam S. {Johnson} and Thambawita, Vajira and Hicks, Steven and Powell, Martine and Michael E. {Lamb} and Gunn Astrid {Baugerud} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {42595,
	title = {Towards an AI-driven talking avatar in virtual reality for investigative interviews of children},
	journal = {GameSys {\textquoteright}22: Proceedings of the 2nd Workshop on Games Systems},
	year = {2022},
	pages = {9-15},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {9781450393812},
	doi = {10.1145/353408510.1145/3534085.3534340},
	url = {https://dl.acm.org/doi/10.1145/3534085.3534340},
	author = {Syed Zohaib {Hassan} and Salehi, Pegah and Ragnhild Klingenber {R{\o}ed} and Halvorsen, P{\r a}l and Gunn Astrid {Baugerud} and Miriam Sinkerud {Johnson} and Lison, Pierre and Riegler, Michael and Michael E. {Lamb} and Griwodz, Carsten and Saeed Shafiee {Sabet}},
	editor = {Claypool, Mark and Eg, Ragnhild}
}
@article {42795,
	title = {Towards the Neuroevolution of Low-level artificial general intelligence},
	journal = {Frontiers in Robotics and AI},
	volume = {9},
	year = {2022},
	month = {10/2022},
	publisher = {Frontiers},
	abstract = {In this work, we argue that the search for Artificial General Intelligence should start from a much lower level than human-level intelligence. The circumstances of intelligent behavior in nature resulted from an organism interacting with its surrounding environment, which could change over time and exert pressure on the organism to allow for learning of new behaviors or environment models. Our hypothesis is that learning occurs through interpreting sensory feedback when an agent acts in an environment. For that to happen, a body and a reactive environment are needed. We evaluate a method to evolve a biologically- inspired artificial neural network that learns from environment reactions named Neuroevolution of Artificial General Intelligence, a framework for low-level artificial general intelligence. This method allows the evolutionary complexification of a randomly-initialized spiking neural network with adaptive synapses, which controls agents instantiated in mutable environments. Such a configuration allows us to benchmark the adaptivity and generality of the controllers. The chosen tasks in the mutable environments are food foraging, emulation of logic gates, and cart-pole balancing. The three tasks are successfully solved with rather small network topologies and therefore it opens up the possibility of experimenting with more complex tasks and scenarios where curriculum learning is beneficial.},
	doi = {10.3389/frobt.2022.1007547},
	url = {https://www.frontiersin.org/articles/10.3389/frobt.2022.1007547/full},
	author = {Pontes-Filho, Sidney and Olsen, Kristoffer and Yazidi, Anis and Riegler, Michael and Halvorsen, P{\r a}l and Nichele, Stefano}
}
@inproceedings {42695,
	title = {Video Analytics in Elite Soccer: A Distributed Computing Perspective},
	journal = {IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)},
	year = {2022},
	month = {06/2022},
	pages = {221-225},
	publisher = {IEEE},
	address = {Trondheim, Norway},
	abstract = {Ubiquitous sensors and Internet of Things (IoT) technologies have revolutionized the sports industry, providing new methodologies for planning, effective coordination of training, and match analysis post game. New methods, including machine learning, image and video processing, have been developed for performance evaluation, allowing the analyst to track the performance of a player in real-time. Following FIFA{\textquoteright}s 2015 approval of electronics performance and tracking system during games, performance data of a single player or the entire team is allowed to be collected using GPS-based wearables. Data from practice sessions outside the sporting arena is being collected in greater numbers than ever before. Realizing the significance of data in professional soccer, this paper presents video analytics, examines recent state-of-the-art literature in elite soccer, and summarizes existing real-time video analytics algorithms. We also discuss real-time crowdsourcing of the obtained data, tactical and technical performance, distributed computing and its importance in video analytics and propose a future research perspective.\&nbsp;},
	keywords = {analytics, football, soccer, Video},
	doi = {10.1109/SAM53842.2022.9827827},
	url = {https://ieeexplore.ieee.org/document/9827827},
	author = {Jha, Debesh and Rauniyar, Ashish and H{\r a}vard D. {Johansen} and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l and Bagci, Ulas}
}
@inproceedings {42710,
	title = {Virtual Reality Talking Avatar for Investigative Interviews of Maltreat Children},
	journal = {19th International Conference on Content-based Multimedia Indexing},
	year = {2022},
	pages = {201-204},
	publisher = {Association for Computing Machinery (ACM)},
	address = {New York, NY, USA},
	isbn = {9781450397209},
	doi = {10.1145/3549555.3549572},
	url = {https://doi.org/10.1145/3549555.3549572},
	author = {Syed Zohaib {Hassan} and Salehi, Pegah and Riegler, Michael and Miriam Sinkerud {Johnson} and Gunn Astrid {Baugerud} and Halvorsen, P{\r a}l and Saeed Shafiee {Sabet}}
}
@misc {42764,
	title = {Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations},
	year = {2022},
	publisher = {arXiv},
	doi = {10.48550/arXiv.2204.00617},
	url = {https://arxiv.org/abs/2204.00617},
	author = {Hicks, Steven and Stor{\r a}s, Andrea and Riegler, Michael and Midoglu, Cise and Hammou, Malek and de Lange, Thomas and Parasa, Sravanthi and Halvorsen, P{\r a}l and Str{\"u}mke, Inga}
}
@article {28311,
	title = {Visual Sentiment Analysis from Disaster Images in Social Media},
	journal = {Sensors},
	volume = {22},
	number = {10},
	year = {2022},
	month = {05/2022},
	pages = {3628},
	publisher = {MDPI},
	abstract = {The increasing popularity of social networks and users{\textquoteright} tendency towards sharing their feelings, expressions, and opinions in text, visual, and audio content have opened new opportunities and challenges in sentiment analysis. While sentiment analysis of text streams has been widely explored in the literature, sentiment analysis from images and videos is relatively new. This article focuses on visual sentiment analysis in a societally important domain, namely disaster analysis in social media. To this aim, we propose a deep visual sentiment analyzer for disaster-related images, covering different aspects of visual sentiment analysis starting from data collection, annotation, model selection, implementation, and evaluations. For data annotation and analyzing people{\textquoteright}s sentiments towards natural disasters and associated images in social media, a crowd-sourcing study has been conducted with a large number of participants worldwide. The crowd-sourcing study resulted in a large-scale benchmark dataset with four different sets of annotations, each aiming at a separate task. The presented analysis and the associated dataset, which is made public, will provide a baseline/benchmark for future research in the domain. We believe the proposed system can contribute toward more livable communities by helping different stakeholders, such as news broadcasters, humanitarian organizations, as well as the general public.},
	doi = {10.3390/s22103628},
	url = {https://doi.org/10.3390/s22103628},
	author = {Syed Zohaib {Hassan} and Ahmad, Kashif and Hicks, Steven and Halvorsen, P{\r a}l and Al-Fuqaha, Ala and Conci, Nicola and Riegler, Michael}
}
@inproceedings {42712,
	title = {When Every Millisecond Counts: The Impact of Delay in VR Gaming},
	journal = {The 14th International Conference on Quality of Multimedia Experience},
	year = {2022},
	publisher = {IEEE},
	doi = {10.1109/QoMEX55416.2022.9900908},
	url = {https://ieeexplore.ieee.org/document/9900908},
	author = {Saeed Shafiee {Sabet} and Eg, Ragnhild and Raaen, Kjetil and Qasim, Muhammed and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {28313,
	title = {AI-Based Video Clipping of Soccer Events},
	journal = {Machine Learning and Knowledge Extraction},
	volume = {3},
	year = {2021},
	month = {12/2021},
	pages = {990 - 1008},
	publisher = {MDPI},
	abstract = {The current gold standard for extracting highlight clips from soccer games is the use of manual annotations and clippings, where human operators define the start and end of an event and trim away the unwanted scenes. This is a tedious, time-consuming, and expensive task, to the extent of being rendered infeasible for use in lower league games. In this paper, we aim to automate the process of highlight generation using logo transition detection, scene boundary detection, and optional scene removal. We experiment with various approaches, using different neural network architectures on different datasets, and present two models that automatically find the appropriate time interval for extracting goal events. These models are evaluated both quantitatively and qualitatively, and the results show that we can detect logo and scene transitions with high accuracy and generate highlight clips that are highly acceptable for viewers. We conclude that there is considerable potential in automating the overall soccer video clipping process.\&nbsp;\&nbsp;},
	doi = {10.3390/make3040049},
	url = {https://www.mdpi.com/2504-4990/3/4/49/pdf},
	author = {Joakim Olav {Valand} and Kadragic, Haris and Hicks, Steven and Thambawita, Vajira and Midoglu, Cise and Kupka, Tomas and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inbook {28006,
	title = {Artificial Intelligence in Gastroenterology},
	booktitle = {Artificial Intelligence in Medicine},
	year = {2021},
	month = {09/2021},
	pages = {1 - 20},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {The holy grail in endoscopy examinations has for a long time been assisted diagnosis using Artificial Intelligence (AI). Recent developments in computer hardware are now enabling technology to equip clinicians with promising tools for computer-assisted diagnosis (CAD) systems. However, creating viable models or architectures, training them, and assessing their ability to diagnose at a human level, are complicated tasks. This is currently an active area of research, and many promising methods have been proposed. In this chapter, we give an overview of the topic. This includes a description of current medical challenges followed by a description of the most commonly used methods in the field. We also present example results from research targeting some of these challenges, and a discussion on open issues and ongoing work is provided. Hopefully, this will inspire and enable readers to future develop CAD systems for gastroenterology.},
	keywords = {Anomaly detection, artificial intelligence, Gastrointestinal endoscopy, Hand-crafted features, Neural Networks, Performance, Semantic segmentation},
	isbn = {978-3-030-58080-3},
	doi = {10.1007/978-3-030-58080-3_163-2},
	url = {https://link.springer.com/referenceworkentry/10.1007\%2F978-3-030-58080-3_163-2},
	author = {Str{\"u}mke, Inga and Hicks, Steven and Thambawita, Vajira and Jha, Debesh and Parasa, Sravanthi and Riegler, Michael and Halvorsen, P{\r a}l},
	editor = {Lidstr{\"o}mer, Niklas and Ashrafian, Hutan}
}
@article {28137,
	title = {Artificial intelligence in the fertility clinic: status, pitfalls and possibilities},
	journal = {Human Reproduction},
	volume = {36},
	year = {2021},
	month = {07/2021},
	pages = {2429 - 2442},
	publisher = {Oxford Academic},
	abstract = {In recent years, the amount of data produced in the field of ART has increased exponentially. The diversity of data is large, ranging from videos to tabular data. At the same time, artificial intelligence (AI) is progressively used in medical practice and may become a promising tool to improve success rates with ART. AI models may compensate for the lack of objectivity in several critical procedures in fertility clinics, especially embryo and sperm assessments. Various models have been developed, and even though several of them show promising performance, there are still many challenges to overcome. In this review, we present recent research on AI in the context of ART. We discuss the strengths and weaknesses of the presented methods, especially regarding clinical relevance. We also address the pitfalls hampering successful use of AI in the clinic and discuss future possibilities and important aspects to make AI truly useful for ART.},
	issn = {0268-1161},
	doi = {10.1093/humrep/deab168},
	url = {https://academic.oup.com/humrep/article/36/9/2429/6330662},
	author = {Riegler, Michael and M H {Stensen} and Witczak, O and J M {Andersen} and Hicks, Steven and Hugo Lewi {Hammer} and Delbarre, E and Halvorsen, P{\r a}l and Yazidi, A and Holst, N and T B {Haugen}}
}
@misc {27862,
	title = {Assessment of sperm motility according to WHO classification using convolutional neural networks},
	year = {2021},
	publisher = {ESHRE},
	address = {ESHRE},
	author = {Trine B. {Haugen} and Hicks, Steven and Witczak, Oliwia and Jorunn M. {Andersen} and Bj{\o}rndal, Lars and Riegler, Michael and Mette Haug {Stensen}}
}
@inproceedings {28315,
	title = {Automated Clipping of Soccer Events using Machine Learning},
	journal = {IEEE International Symphosium of Multimedia (ISM)},
	year = {2021},
	month = {12/2021},
	publisher = { IEEE},
	doi = {10.1109/ISM52913.2021.00042},
	author = {Valand, Joakim and Kadragic, Haris and Hicks, Steven and Thambawita, Vajira and Midoglu, Cise and Kupka, Tomas and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {28314,
	title = {Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities},
	journal = {Machine Learning and Knowledge Extraction},
	volume = {3},
	year = {2021},
	month = {12/2021},
	pages = {1030 - 1054},
	publisher = {MDPI},
	abstract = {Detecting events in videos is a complex task, and many different approaches, aimed at a large variety of use-cases, have been proposed in the literature. Most approaches, however, are unimodal and only consider the visual information in the videos. This paper presents and evaluates different approaches based on neural networks where we combine visual features with audio features to detect (spot) and classify events in soccer videos. We employ model fusion to combine different modalities such as video and audio, and test these combinations against different state-of-the-art models on the SoccerNet dataset. The results show that a multimodal approach is beneficial. We also analyze how the tolerance for delays in classification and spotting time, and the tolerance for prediction accuracy, influence the results. Our experiments show that using multiple modalities improves event detection performance for certain types of events.},
	doi = {10.3390/make3040051},
	url = {https://www.mdpi.com/2504-4990/3/4/51/pdf},
	author = {Olav Andre Nerg {Rongved} and Stige, Markus and Hicks, Steven and Thambawita, Vajira and Midoglu, Cise and Zouganeli, Evi and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {27774,
	title = {A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging},
	journal = {Medical Image Analysis},
	volume = {70},
	year = {2021},
	pages = {102007},
	publisher = {Elsevier},
	abstract = {Gastrointestinal endoscopy has\&nbsp;been\&nbsp;an\&nbsp;active field\&nbsp;of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. \&nbsp; The high missed rate of such abnormalities during endoscopy is thus a\&nbsp; critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic\&nbsp; GI\&nbsp; disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the\&nbsp; Medico\&nbsp; GI\&nbsp; challenges:\&nbsp; Medical\&nbsp; Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018,\&nbsp; and\&nbsp; BioMedia\&nbsp; ACM\&nbsp; MM\&nbsp; Grand\&nbsp; Challenge\&nbsp; 2019.\&nbsp; These challenges are initiative to set-up\&nbsp; a\&nbsp; benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a\&nbsp; period of three consecutive years and provide a\&nbsp; detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings.\&nbsp; Our analysis revealed that the participants achieved an improvement on maximum\&nbsp; Mathew correlation coefficient\&nbsp; (MCC)\&nbsp; from\&nbsp; 82.68\%\&nbsp; in\&nbsp; 2017\&nbsp; to\&nbsp; 93.98\%\&nbsp; in\&nbsp; 2018\&nbsp; and 95.20\% in 2019 challenges, and a significant increase in computational speed over consecutive years.},
	keywords = {Artificial intelligence, BioMedia 2019 Grand Challenge, Computer-aided detection and diagnosis, Gastrointestinal endoscopy challenges, Medical imaging, Medico Task 2017, Medico Task 2018},
	doi = {https://doi.org/10.1016/j.media.2021.102007},
	author = {Jha, Debesh and Ali, Sharib and Hicks, Steven and Thambawita, Vajira and Borgli, Hanna and Pia H. {Smedsrud} and de Lange, Thomas and Pogorelov, Konstantin and Wang, Xiaowei and Harzig, Philipp and Tran, Minh-Triet and Meng, Wenhua and Hoang, Trung-Hieu and Dias, Danielle and Tobey H. {Ko} and Agrawal, Taruna and Ostroukhova, Olga and Khan, Zeshan and Muhammad Atif {Tahir} and Liu, Yang and Chang, Yuan and Kirker{\o}d, Mathias and Johansen, Dag and Lux, Mathias and H{\r a}vard D. {Johansen} and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {27640,
	title = {A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation},
	journal = {IEEE Journal of Biomedical and Health Informatics},
	volume = {25},
	year = {2021},
	pages = {2029 - 2040},
	publisher = { IEEE},
	abstract = {Colonoscopy is considered the gold standard for detection of colorectal cancer and its precursors. Existing examination methods are, however, hampered by high overall miss-rate, and many abnormalities are left undetected. Computer-Aided Diagnosis systems based on advanced machine learning algorithms are touted as a game-changer that can identify regions in the colon overlooked by the physicians during endoscopic examinations, and help detect and characterize lesions. In previous work, we have proposed the ResUNet++ architecture and demonstrated that it produces more efficient results compared with its counterparts U-Net and ResUNet. In this paper, we demonstrate that further improvements to the overall prediction performance of the ResUNet++ architecture can be achieved by using Conditional Random Field (CRF) and Test-Time Augmentation (TTA). We have performed extensive evaluations and validated the improvements using six publicly available datasets: Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETIS-Larib PolypDB, ASU-Mayo Clinic Colonoscopy Video Database, and CVC-VideoClinicDB. Moreover, we compare our proposed architecture and resulting model with other State-of-the-art methods. To explore the generalization capability of ResUNet++ on different publicly available polyp datasets, so that it could be used in a real-world setting, we performed an extensive cross-dataset evaluation. The experimental results show that applying CRF andTTA improves the performance on various polyp segmentation datasets both on the same dataset and cross-dataset. To check the model{\textquoteright}s performance on difficult to detect polyps, we selected, with the help of an expert gastroenterologist,196sessile or flat polyps that are less than ten millimeters in size. This additional data has been made available as a subset of Kvasir-SEG. Our approaches showed good results for flat or sessile and smaller polyps, which are known to be one of the major reasons for high polyp miss-rates. This is one of the significant strengths of our work and indicates that our methods should be investigated further for use in clinical practice.},
	keywords = {colonoscopy, conditional random field, generalization, Polyp segmentation, ResUNet++, test-time augmentation},
	doi = {10.1109/JBHI.2021.3049304},
	author = {Jha, Debesh and Pia H. {Smedsrud} and Johansen, Dag and de Lange, Thomas and H{\r a}vard D. {Johansen} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {27831,
	title = {Data Augmentation Using Generative Adversarial Networks For Creating Realistic Artificial Colon Polyp Images},
	journal = {DDW 2021},
	year = {2021},
	author = {Thambawita, Vajira and Str{\"u}mke, Inga and Hicks, Steven and Riegler, Michael and Halvorsen, P{\r a}l and Parasa, Sravanthi}
}
@inproceedings {27635,
	title = {DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation},
	journal = {25th International Conference on Pattern Recognition},
	year = {2021},
	pages = {307-314},
	publisher = { Springer},
	abstract = {Colonoscopy is the gold standard for examination and detection of colorectal polyps. Localization and delineation of polyps can play a vital role in treatment (e.g., surgical planning) and prognostic decision making. Polyp segmentation can provide detailed boundary information for clinical analysis. Convolutional neural networks have improved the performance in colonoscopy. However, polyps usually possess various challenges, such as intra-and inter-class variation and noise. While manual labeling for polyp assessment requires time from experts and is prone to human error (e.g., missed lesions), an automated, accurate, and fast segmentation can improve the quality of delineated lesion boundaries and reduce missed rate. The Endotect challenge provides an opportunity to benchmark computer vision methods by training on the publicly available Hyperkvasir and testing on a separate unseen dataset. In this paper, we propose a novel architecture called {\textquoteleft}{\textquoteleft}DDANet{\textquoteright}{\textquoteright} based on a dual decoder attention network. Our experiments demonstrate that the model trained on the Kvasir-SEG dataset and tested on an unseen dataset achieves a dice coefficient of 0.7874, mIoU of 0.7010, recall of 0.7987, and a precision of 0.8577, demonstrating the generalization ability of our model.},
	keywords = {Benchmarking, Convolutional neural network, deep learning, Polyp segmentation},
	author = {Nikhil Kumar {Tomar} and Jha, Debesh and Ali, Sharib and H{\r a}vard D. {Johansen} and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {kanters2021deep,
	title = {Deep learning neural network can measure ECG intervals and amplitudes accurately},
	journal = {Journal of Electrocardiology},
	volume = {69},
	year = {2021},
	pages = {82},
	publisher = {Elsevier},
	author = {Jorgen K {Kanters} and Hicks, Steven and Jonas L {Isaksen} and Grarup, Niels and Niels-Henrik Holstein {Rathlou} and Ghouse, Jonas and Ahlberg, Gustav and Morten Salling {Olesen} and Linneberg, Allan and Ellervik, Christina and Halvorsen, P{\r a}l and Riegler, Michael}
}
@article {Thambawita2021.04.27.21256189,
	title = {DeepFake electrocardiograms: the key for open science for artificial intelligence in medicine},
	journal = {Scientific Reports},
	year = {2021},
	publisher = {Nature Publishing Group},
	abstract = {Recent global developments underscore the prominent role big data have in modern medical science. Privacy issues are a prevalent problem for collecting and sharing data between researchers. Synthetic data generated to represent real data carrying similar information and distribution may alleviate the privacy issue.In this study, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 12-lead 10-sec electrocardiograms (ECGs). We have developed and compare two methods, WaveGAN* and Pulse2Pulse GAN. We trained the GANs with 7,233 real normal ECG to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. These synthetic ECGs are fully anonymous and cannot be referred to any individual, hence they may be used freely. The synthetic dataset will be available as open access for researchers at OSF.io and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs.In conclusion, we were able to generate realistic synthetic ECGs using adversarial neural networks on normal ECGs from two population studies, i.e., there by addressing the relevant privacy issues in medical datasets.Competing Interest StatementThe authors have declared no competing interest.Clinical TrialN/AFunding StatementThis work is funded in part by Novo Nordisk Foundation project number NNF18CC0034900.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:We confirm that all experiments were performed in accordance with Helsinki guidelines and regulations of the Danish Regional Committees for Medical and Health Research Ethics. The data studies were approved by the ethical committee of Region Zealand (SJ-113, SJ-114, SJ-191), ethical committee of Copenhagen Amt (KA 98 155).All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe Normal DeepFake ECGs are available at OSF (https://osf.io/6hved/) with corresponding MUSE 12SL ground truth values freely downloadable and usable for ECG algorithm development. The DeepFake generative model is available at https://pypi.org/project/deepfake-ecg/ to generate only synthetic ECGs. https://osf.io/6hved/ https://pypi.org/project/deepfake-ecg/},
	doi = {10.1101/2021.04.27.21256189},
	url = {https://www.medrxiv.org/content/early/2021/05/10/2021.04.27.21256189.1},
	author = {Thambawita, Vajira and Jonas L. {Isaksen} and Hicks, Steven and Ghouse, Jonas and Ahlberg, Gustav and Linneberg, Allan and Grarup, Niels and Ellervik, Christina and Morten Salling {Olesen} and Hansen, Torben and Graff, Claus and Holstein-Rathlou, Niels-Henrik and Str{\"u}mke, Inga and Hugo Lewi {Hammer} and Maleckar, Molly and Halvorsen, P{\r a}l and Riegler, Michael and J{\o}rgen K. {Kanters}}
}
@article {28214,
	title = {DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine},
	journal = {Nature Scientific Reports },
	volume = {11},
	year = {2021},
	month = {09/2021},
	pages = {21896},
	publisher = { Springer nature},
	abstract = {Recent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem for collecting and sharing data between researchers. However, synthetic data generated to represent real data carrying similar information and distribution may alleviate the privacy issue. In this study, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 10-s 12-lead electrocardiograms (ECGs). We have developed and compared two methods, named WaveGAN* and Pulse2Pulse. We trained the GANs with 7,233 real normal ECGs to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN* to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. Although these synthetic ECGs mimic the dataset used for creation, the ECGs are not linked to any individuals and may thus be used freely. The synthetic dataset will be available as open access for researchers at OSF.io and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs. In conclusion, we were able to generate realistic synthetic ECGs using generative adversarial neural networks on normal ECGs from two population studies, thereby addressing the relevant privacy issues in medical datasets.},
	doi = {https://doi.org/10.1038/s41598-021-01295-2},
	url = {https://www.nature.com/articles/s41598-021-01295-2},
	author = {Thambawita, Vajira and Jonas L. {Isaksen} and Hicks, Steven and Ghouse, Jonas and Ahlberg, Gustav and Linneberg, Allan and Grarup, Niels and Ellervik, Christina and Morten Salling {Olesen} and Hansen, Torben and Graff, Claus and Holstein-Rathlou, Niels-Henrik and Str{\"u}mke, Inga and Hugo Lewi {Hammer} and Maleckar, Molly and Halvorsen, P{\r a}l and Riegler, Michael and J{\o}rgen K. {Kanters}}
}
@inproceedings {27834,
	title = {DeepSynthBody: the beginning of the end for data deficiency in medicine},
	journal = {The International Conference on Applied Artificial Intelligence (ICAPAI)},
	year = {2021},
	publisher = {IEEE},
	abstract = {Limited access to medical data is a barrier on developing new and efficient machine learning solutions in medicine such as computer-aided diagnosis, risk assessments, predicting optimal treatments and home-based personal healthcare systems. This paper presents DeepSynthBody: a novel framework that overcomes some of the inherent restrictions and limitations of medical data by using deep generative adversarial networks to produce synthetic data with characteristics similar to the real data, so-called DeepSynth (deep synthetic) data. We show that DeepSynthBody can address two key issues commonly associated with medical data, namely privacy concerns (as a result of data protection rules and regulations) and the high costs of annotations. To demonstrate the full pipeline of applying DeepSynthBody concepts and user-friendly functionalities, we also describe a synthetic medical dataset generated and published using our framework. DeepSynthBody opens a new era of machine learning applications in medicine with a synthetic model of the human body.},
	doi = {10.1109/ICAPAI49758.2021.9462062},
	author = {Thambawita, Vajira and Hicks, Steven and Isaksen, Jonas and Mette Haug {Stensen} and Trine B {Haugen} and Kanters, J{\o}rgen and Parasa, Sravanthi and de Lange, Thomas and H{\r a}vard D. {Johansen} and Johansen, Dag and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {27833,
	title = {DivergentNets: Medical Image Segmentation by Network Ensemble},
	journal = {EndoCV},
	year = {2021},
	abstract = {Detection of colon polyps has become a trending topic in the intersecting fields of machine learning and gastrointestinal endoscopy. The focus has mainly been on per-frame classification. More recently, polyp segmentation has gained attention in the medical community. Segmentation has the advantage of being more accurate than per-frame classification or object detection as it can show the affected area in greater detail. For our contribution to the EndoCV 2021 segmentation challenge, we propose two separate approaches. First, a segmentation model named TriUNet\&nbsp;composed of three separate UNet models. Second, we combine TriUNet with an ensemble of well-known segmentation models, namely UNet++, FPN, DeepLabv3, and DeepLabv3+, into a model called DivergentNets\&nbsp;to produce more generalizable medical image segmentation masks. In addition, we propose a modified Dice loss that calculates loss only for a single class when performing multi-class segmentation, forcing the model to focus on what is most important. Overall, the proposed methods achieved the best average scores for each respective round in the challenge, with TriUNet being the winning model in Round I\&nbsp;and DvergentNets being the winning model in Round II\&nbsp;of the segmentation generalization challenge at EndoCV 2021},
	author = {Thambawita, Vajira and Hicks, Steven and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {28095,
	title = {Dutkat: A Multimedia System for Catching Illegal Catchers in a Privacy-Preserving Manner},
	journal = {Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval (ICDAR{\textquoteright}21)},
	year = {2021},
	pages = {57-61},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {Fish crime is considered a global and serious problem for a healthy and sustainable development of one of mankind{\textquoteright}s important sources of food. Technological surveillance and control solutions are emerging as remedies to combat criminal activities, but such solutions might also come with impractical and negative side-effects and challenges. In this paper, we present the concept and design of a surveillance system in lieu of current surveillance trends striking a delicate balance between privacy of legal actors while simultaneously capturing evidence-based footage, sensory data, and forensic proofs of illicit activities. Our proposed novel approach is to assist human operators in the 24/7 surveillance loop of remote professional fishing activities with a privacy-preserving Artificial Intelligence (AI) surveillance system operating in the same proximity as the activities being surveyed. The system will primarily be using video surveillance data, but also other sensor data captured on the fishing vessel. Additionally, the system correlates with other sources such as reports from other fish catches in the approximate area and time, etc. Only upon true positive flagging of specific potentially illicit activities by the locally executing AI algorithms, can forensic evidence be accessed from this physical edge, the fishing vessel. Besides a more privacy-preserving solution, our edge-based AI system also benefits from much less data that has to be transferred over unreliable, low-bandwidth satellite-based networks.},
	isbn = {9781450385299},
	doi = {10.1145/346394410.1145/3463944.3469102},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3463944},
	author = {Tor-Arne Schmidt {Nordmo} and Aril B. {Ovesen} and H{\r a}vard D. {Johansen} and Riegler, Michael and Halvorsen, P{\r a}l and Johansen, Dag},
	editor = {Dao, Minh-Son and Dang-Nguyen, Duc-Tien and Riegler, Michael}
}
@article {27866,
	title = {Explaining deep neural networks for knowledge discovery in electrocardiogram analysis},
	journal = {Scientific Reports},
	volume = {11},
	year = {2021},
	month = {05/2021},
	pages = {10949},
	publisher = {Springer Nature},
	abstract = {Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.},
	doi = {10.1038/s41598-021-90285-5},
	url = {http://www.nature.com/articles/s41598-021-90285-5h},
	author = {Hicks, Steven and Jonas L. {Isaksen} and Thambawita, Vajira and Ghouse, Jonas and Ahlberg, Gustav and Linneberg, Allan and Grarup, Niels and Str{\"u}mke, Inga and Ellervik, Christina and Morten Salling {Olesen} and Hansen, Torben and Graff, Claus and Holstein-Rathlou, Niels-Henrik and Halvorsen, P{\r a}l and Maleckar, Molly and Riegler, Michael and J{\o}rgen K. {Kanters}}
}
@inproceedings {28020,
	title = {Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy},
	journal = {2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)},
	year = {2021},
	pages = {1-4},
	publisher = {IEEE},
	abstract = {Minimally Invasive Surgery (MIS) is a surgical intervention used to examine the organs inside the abdomen and has been widely used due to its effectiveness over open surgery. Due to the hardware improvements such as high definition cameras, this procedure has significantly improved and new software methods have open potential for computer-assisted procedures. However, there exists challenges and requirement to improve detection and tracking of the position of the instruments during these surgical procedures. To this end, we evaluate and compare some popular deep learning methods that can potentially be explored for the automated segmentation of surgical instruments in laparoscopy, an important step towards tool tracking. Our experimental results demonstrate that the Dual decoder attention network (DDANet) produces a superior result compared to other recent deep learning methods. DDANet produces a dice coefficient of 0.8739 and mean intersection over union of 0.8183 for the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge 2019 dataset, at a real-time speed of 101.36 frames per second which is critical for such procedures.},
	keywords = {Real-time segmentation, minimally invasive surgery, surgical instruments,  laparoscopy, deep learning},
	author = {Jha, Debesh and Ali, Sharib and Nikhil Kumar {Tomar} and Riegler, Michael and Johansen, Dag and H{\r a}vard D {Johansen} and Halvorsen, P{\r a}l}
}
@article {28094,
	title = {File System Support for Privacy-Preserving Analysis and Forensics in Low-Bandwidth Edge Environments},
	journal = {Information},
	volume = {12},
	year = {2021},
	month = {10/2021},
	pages = {430},
	publisher = {MDPI},
	abstract = {In this paper, we present initial results from our distributed edge systems research in the domain of sustainable harvesting of common good resources in the Arctic Ocean. Specifically, we are developing a digital platform for real-time privacy-preserving sustainability management in the domain of commercial fishery surveillance operations. This is in response to potentially privacy-infringing mandates from some governments to combat overfishing and other sustainability challenges. Our approach is to deploy sensory devices and distributed artificial intelligence algorithms on mobile, offshore fishing vessels and at mainland central control centers. To facilitate this, we need a novel data plane supporting efficient, available, secure, tamper-proof, and compliant data management in this weakly connected offshore environment. We have built our first prototype of Dorvu, a novel distributed file system in this context. Our devised architecture, the design trade-offs among conflicting properties, and our initial experiences are further detailed in this paper.\&nbsp;\&nbsp;},
	doi = {10.3390/info12100430},
	url = {https://www.mdpi.com/2078-2489/12/10/430},
	author = {Aril Bernhard {Ovesen} and Tor-Arne Schmidt {Nordmo} and H{\r a}vard Dagenborg {Johansen} and Riegler, Michael and Halvorsen, P{\r a}l and Johansen, Dag}
}
@inproceedings {27641,
	title = {HTAD: A Home-Tasks Activities Dataset with Wrist-accelerometer and Audio Features},
	journal = {27th International Conference on Multimedia Modeling},
	year = {2021},
	pages = {196-205},
	publisher = { Springer},
	keywords = {Accelerometer, Activity  recognition, Audio, dataset, Sensor fusion},
	author = {Garcia-Ceja, Enrique and Thambawita, Vajira and Hicks, Steven and Jha, Debesh and Jakobsen, Petter and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {28136,
	title = {HYPERAKTIV: An Activity Dataset from Patients with Attention-Deficit/Hyperactivity Disorder (ADHD)},
	journal = {Proceedings of the 12th ACM Multimedia Systems Conference (MMSys {\textquoteright}21)},
	year = {2021},
	pages = {314{\textendash}319},
	publisher = {ACM},
	abstract = {Machine learning research within healthcare frequently lacks the public data needed to be fully reproducible and comparable. Datasets are often restricted due to privacy concerns and legal requirements that come with patient-related data. Consequentially, many algorithms and models get published on the same topic without a standard benchmark to measure against. Therefore, this paper presents HYPERAKTIV, a public dataset containing health, activity, and heart rate data from patients diagnosed with attention deficit hyperactivity disorder, better known as ADHD. The dataset consists of data collected from 51 patients with ADHD and 52 clinical controls. In addition to the activity and heart rate data, we also include a series of patient attributes such as their age, sex, and information about their mental state, as well as output data from a computerized neuropsychological test. Together with the presented dataset, we also provide baseline experiments using traditional machine learning algorithms to predict ADHD based on the included activity data. We hope that this dataset can be used as a starting point for computer scientists who want to contribute to the field of mental health, and as a common benchmark for future work in ADHD analysis.},
	doi = {10.1145/3458305.3478454},
	url = {https://dl.acm.org/doi/10.1145/3458305.3478454},
	author = {Hicks, Steven and Stautland, Andrea and Ole Bernt {Fasmer} and Fr{\o}land, Wenche and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Mjeldheim, Kristin and Ketil J. {Oedegaard} and Osnes, Berge and Vigdis Elin Gi{\ae}v {Syrstad} and Riegler, Michael and Jakobsen, Petter}
}
@inproceedings {27830,
	title = {Identification of spermatozoa by unsupervised learning from video data},
	journal = {37th Virtual Annual Meeting of the European Society of Human Reproduction and Embryology (ESHRE)},
	year = {2021},
	publisher = {Oxford University Press},
	abstract = {Identification of individual sperm is essential to assess a given sperm sample{\textquoteright}s motility behavior. Existing computer-aided systems need training data based on annotations by professionals, which is resource demanding. On the other hand, data analysed by unsupervised machine learning algorithms can improve supervised algorithms that are more stable for clinical applications. Therefore, unsupervised sperm identification can improve computer-aided sperm analysis systems predicting different aspects of sperm samples. Other possible applications are assessing kinematics and counting of spermatozoa.\&nbsp;},
	author = {Thambawita, Vajira and Trine B {Haugen} and Stensen, M.H. and Witczak, Oliwia and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {27832,
	title = {Impact of Image Resolution on Convolutional Neural Networks Performance in Gastrointestinal Endoscopy},
	journal = {DDW 2021},
	year = {2021},
	abstract = {Convolutional neural networks (CNNs) are increasingly used to improve and automate processes in gastroenterology, like the detection of polyps during a colonoscopy. An important input to these methods is images and videos. Up until now, no well-defined, common understanding or standard regarding the resolution of the images and video frames has been defined, and to reduce processing time and resource requirements, images are today almost always down-sampled. However, how such down-sampling and the image resolution influence the performance in context with medical data is unknown. In this work, we investigate how the resolution relates to the performance of convolutional neural networks. This can help set standards for image or video characteristics for future CNN based models in gastrointestinal endoscopy.},
	author = {Thambawita, Vajira and Hicks, Steven and Str{\"u}mke, Inga and Riegler, Michael and Halvorsen, P{\r a}l and Parasa, Sravanthi}
}
@article {28215,
	title = {Impact of Image Resolution on Deep Learning Performance in Endoscopy Image Classification: An Experimental Study Using a Large Dataset of Endoscopic Images},
	journal = {Diagnostics},
	volume = {11},
	year = {2021},
	month = {09/2021},
	publisher = {MDPI},
	abstract = {Recent trials have evaluated the efficacy of deep convolutional neural network (CNN)-based AI systems to improve lesion detection and characterization in endoscopy. Impressive results are achieved, but many medical studies use a very small image resolution to save computing resources at the cost of losing details. Today, no conventions between resolution and performance exist, and monitoring the performance of various CNN architectures as a function of image resolution provides insights into how subtleties of different lesions on endoscopy affect performance. This can help set standards for image or video characteristics for future CNN-based models in gastrointestinal (GI) endoscopy. This study examines the performance of CNNs on the HyperKvasir dataset, consisting of 10,662 images from 23 different findings. We evaluate two CNN models for endoscopic image classification under quality distortions with image resolutions ranging from 32 {\texttimes} 32 to 512 {\texttimes} 512 pixels. The performance is evaluated using two-fold cross-validation and F1-score, maximum Matthews correlation coefficient (MCC), precision, and sensitivity as metrics. Increased performance was observed with higher image resolution for all findings in the dataset. MCC was achieved at image resolutions between 512 {\texttimes} 512 pixels for classification for the entire dataset after including all subclasses. The highest performance was observed with an MCC value of 0.9002 when the models were trained on the highest resolution and tested on the same resolution. Different resolutions and their effect on CNNs are explored. We show that image resolution has a clear influence on the performance which calls for standards in the field in the future.},
	doi = {https://doi.org/10.3390/diagnostics11122183},
	url = {https://www.mdpi.com/2075-4418/11/12/2183},
	author = {Thambawita, Vajira and Str{\"u}mke, Inga and Hicks, Steven and Halvorsen, P{\r a}l and Parasa, Sravanthi and Riegler, Michael}
}
@misc {42547,
	title = {KUNSTIG INTELLIGENS I KLINIKKEN - Seks trender for fremtidens helsetjeneste},
	year = {2021},
	publisher = {Teknologiradet},
	address = {Oslo, Norway},
	isbn = {978-82-92447-013-8},
	url = {https://teknologiradet.no/wp-content/uploads/sites/105/2022/02/Kunstig-intelligens-i-klinikken.pdf},
	author = {Anne Siri Koksr {Bekkelund} and Lovett, Hilde and Tenn{\o}e, Tore and Nassehi, Damoun and Helga M. {Br{\o}gger} and Riegler, Michael and Fosse, Erik and Madsen, Steinar}
}
@article {27867,
	title = {Kvasir-Capsule, a video capsule endoscopy dataset},
	journal = {Scientific Data},
	volume = {8},
	year = {2021},
	pages = {142},
	publisher = {Springer Nature},
	abstract = {Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present\&nbsp;Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital.\&nbsp;Kvasir-Capsule\&nbsp;consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The\&nbsp;Kvasir-Capsule\&nbsp;dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.},
	doi = {10.1038/s41597-021-00920-z},
	url = {http://www.nature.com/articles/s41597-021-00920-z},
	author = {Pia H. {Smedsrud} and Thambawita, Vajira and Hicks, Steven and Gjestang, Henrik and Oda Olsen {Nedrejord} and N{\ae}ss, Espen and Borgli, Hanna and Jha, Debesh and Tor Jan Derek {Berstad} and Sigrun L. {Eskeland} and Lux, Mathias and Espeland, H{\r a}vard and Petlund, Andreas and Duc Tien Dang {Nguyen} and Garcia-Ceja, Enrique and Johansen, Dag and Peter T. {Schmidt} and Toth, Ervin and Hugo Lewi {Hammer} and de Lange, Thomas and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {27511,
	title = {Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy},
	journal = {27th International Conference on Multimedia Modeling},
	volume = {LNCS, volume 12573},
	year = {2021},
	pages = {218-229},
	publisher = {Springer},
	abstract = {Gastrointestinal (GI) pathologies are periodically screened, biopsied, and resected using surgical tools. Usually the procedures and the treated or resected areas are not specifically tracked or analysed during or after colonoscopies. Information regarding disease borders, development and amount and size of the resected area get lost. This can lead to poor follow-up and bothersome reassessment difficulties post-treatment. To improve the current standard and also to foster more research on the topic we have released the\&nbsp; {\textquoteleft}{\textquoteleft}Kvasir Instrument{\textquoteright}{\textquoteright} dataset which consists of $590$ annotated frames containing GI procedure tools such as snares, balloons and biopsy forceps, etc. Beside of the images, the dataset includes ground truth masks and bounding boxes and has been verified by two expert GI endoscopists. Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development. We obtained a dice coefficient score of 0.9158 and a Jaccard index of 0.8578 using a classical U-Net architecture. A similar dice coefficient score was observed for DoubleUNet. The qualitative results showed that the model did not work for the images with specularity and the frames with multiple instruments, while the best result for both methods was observed on all other types of images. Both, qualitative and quantitative results show that the model performs reasonably good, but there is a large potential for further improvements.\&nbsp; Benchmarking using the dataset provides an opportunity for researchers to contribute to the field of automatic endoscopic diagnostic and therapeutic tool segmentation for GI endoscopy.\&nbsp;},
	author = {Jha, Debesh and Ali, Sharib and Emanuelsen, Krister and Hicks, Steven and Thambawita, Vajira and Garcia-Ceja, Enrique and Riegler, Michael and de Lange, Thomas and Peter T. {Schmidt} and H{\r a}vard D. {Johansen} and Johansen, Dag and Halvorsen, P{\r a}l}
}
@article {27701,
	title = {Large scale {\textquotedblleft}speedtest{\textquotedblright} experimentation in Mobile Broadband Networks},
	journal = {Computer Networks},
	volume = {184},
	year = {2021},
	month = {Oct-17-2021},
	pages = {107629},
	publisher = {Elsevier},
	issn = {13891286},
	doi = {10.1016/j.comnet.2020.107629},
	author = {Midoglu, Cise and Kousias, Konstantinos and Alay, {\"O}zg{\"u} and Lutu, Andra and Argyriou, Antonios and Riegler, Michael and Griwodz, Carsten}
}
@article {28228,
	title = {MedAI: Transparency in Medical Image Segmentation},
	journal = {Nordic Machine Intelligence},
	volume = {1},
	year = {2021},
	month = {Jan-11-2021},
	pages = {1 - 4},
	publisher = {NMI},
	address = {Oslo},
	doi = {10.5617/nmi.9140},
	url = {https://journals.uio.no/NMI/article/view/9140https://journals.uio.no/NMI/article/download/9140/7753https://journals.uio.no/NMI/article/download/9140/7753},
	author = {Hicks, Steven and Jha, Debesh and Thambawita, Vajira and Halvorsen, P{\r a}l and Singstad, Bj{\o}rn-Jostein and Gaur, Sachin and Pettersen, Klas and Goodwin, Morten and Parasa, Sravanthi and de Lange, Thomas and Riegler, Michael}
}
@inproceedings {40625,
	title = {Medico Multimedia Task at MediaEval 2021: Transparency in Medical Image Segmentation},
	journal = {Mediaeval Medico 2021},
	year = {2021},
	publisher = {Mediaeval 2021},
	abstract = {The Medico Multimedia Task\&nbsp;focuses on providing multimedia researchers with the opportunity to contribute to different areas of medicine using multimedia data to solve several subtasks. This year, the task focuses on transparency within machine learning-based medical segmentation systems, where the use case is gastrointestinal endoscopy. In this paper, we motivate the organization of this task, describe the development and test dataset, and present the evaluation process used to assess the participants{\textquoteright} submissions.},
	url = {https://2021.multimediaeval.com/},
	author = {Hicks, Steven and Jha, Debesh and Thambawita, Vajira and Hugo Lewi {Hammer} and de Lange, Thomas and Parasa, Sravanthi and Riegler, Michael and Halvorsen, P{\r a}l}
}
@misc {aneja2021mmsys21,
	title = {MMSys{\textquoteright}21 Grand Challenge on Detecting Cheapfakes},
	year = {2021},
	publisher = {arXiv},
	author = {Aneja, Shivangi and Midoglu, Cise and Dang-Nguyen, Duc-Tien and Riegler, Michael and Halvorsen, P{\r a}l and Niessner, Matthias and Adsumilli, Balu and Bregler, Chris}
}
@inproceedings {28008,
	title = {Multimodal Virtual Avatars for Investigative Interviews with Children},
	journal = {Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval (ICDAR {\textquoteright}21)},
	year = {2021},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {In this article, we present our ongoing work in the field of training police officers who conduct interviews with abused children. The objectives in this context are to protect vulnerable children from abuse, facilitate prosecution of offenders, and ensure that innocent adults are not accused of criminal acts. There is therefore a need for more data that can be used for improved interviewer training to equip police with the skills to conduct high-quality interviews. To support this important task, we propose to research a training program that utilizes different system components and multimodal data from the field of artificial intelligence such as chatbots, generation of visual content, text-to-speech, and speech-to-text. This program will be able to generate an almost unlimited amount of interview and also training data. The goal of combining all these different technologies and datatypes is to create an immersive and interactive child avatar that responds in a realistic way, to help to support the training of police interviewers, but can also produce synthetic data of interview situations that can be used to solve different problems in the same domain.},
	isbn = {9781450385299},
	doi = {10.1145/346394410.1145/3463944.3469269},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3463944},
	author = {Gunn Astrid {Baugerud} and Miriam S. {Johnson} and Ragnhild Klingenber {R{\o}ed} and Michael E. {Lamb} and Powell, Martine and Thambawita, Vajira and Hicks, Steven and Salehi, Pegah and Syed Zohaib {Hassan} and Halvorsen, P{\r a}l and Riegler, Michael},
	editor = {Dao, Minh-Son and Dang-Nguyen, Duc-Tien and Riegler, Michael}
}
@inproceedings {27856,
	title = {NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy},
	journal = {34th IEEE CBMS International Symposium on Computer-Based Medical Systems},
	year = {2021},
	publisher = { IEEE},
	abstract = {Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and be helpful to assess lesions more accurately. To this extent, semantic segmentation methods that can perform automated real-time delineation of a region-of-interest, e.g., boundary identification of cancer or precancerous lesions, can benefit both diagnosis and interventions. However, accurate and real-time segmentation of endoscopic images is extremely challenging due to its high operator dependence and high-definition image quality. To utilize automated methods in clinical settings, it is crucial to design lightweight models with low latency such that they can be integrated with low-end endoscope hardware devices. In this work, we propose NanoNet, a novel architecture for the segmentation of video capsule endoscopy and colonoscopy images. Our proposed architecture allows real-time performance and has higher segmentation accuracy compared to other more complex ones. We use video capsule endoscopy and standard colonoscopy datasets with polyps, and a dataset consisting of endoscopy biopsies and surgical instruments, to evaluate the effectiveness of our approach. Our experiments demonstrate the increased performance of our architecture in terms of a trade-off between model complexity, speed, model parameters, and metric performances. Moreover, the resulting model size is relatively tiny, with only nearly 36,000 parameters compared to traditional deep learning approaches having millions of parameters.},
	keywords = {colonoscopy, deep learning, segmentation, tool  segmentation, Video capsule endoscopy},
	author = {Jha, Debesh and Nikhil Kumar {Tomar} and Ali, Sharib and Riegler, Michael and H{\r a}vard D {Johansen} and Johansen, Dag and de Lange, Thomas and Halvorsen, P{\r a}l}
}
@inproceedings {28229,
	title = {Njord: An out-in-the-wild real world fish vessel catch analysis dataset},
	journal = {Arctic Frontiers},
	year = {2021},
	publisher = {Arctic Frontiers},
	address = {Troms{\o}, Norway},
	author = {Riegler, Michael and Johansen, Dag and Bj{\o}rn Aslak {Juliussen} and Tor-Arne Schmidt {Nordmo} and Aril Bernhard {Ovesen} and Halvorsen, P{\r a}l and H{\r a}vard D. {Johansen} and Jon Petter {Rui}}
}
@inproceedings {39299,
	title = {PAANet: Progressive Alternating Attention for Automatic Medical Image Segmentation},
	journal = {Proc. of 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)},
	year = {2021},
	month = {12/2021},
	publisher = {IEEE},
	address = {Paris / Cr{\'e}teil, France},
	abstract = {Medical image segmentation can provide detailed information for clinical analysis which can be useful for scenarios where the detailed location of a finding is important. Knowing the location of a disease can play a vital role in treatment and decision-making. Convolutional neural network (CNN) based encoder-decoder techniques have advanced the performance of automated medical image segmentation systems. Several such CNN-based methodologies utilize techniques such as spatial- and channel-wise attention to enhance performance. Another technique that has drawn attention in recent years is residual dense blocks (RDBs). The successive convolutional layers in densely connected blocks are capable of extracting diverse features with varied receptive fields and thus, enhancing performance. However, consecutive stacked convolutional operators may not necessarily generate features that facilitate the identification of the target structures. In this paper, we propose a progressive alternating attention network (PAANet). We develop progressive alternating attention dense (PAAD) blocks, which construct a guiding attention map (GAM) after every convolutional layer in the dense blocks using features from all scales. The GAM allows the following layers in the dense blocks to focus on the spatial locations relevant to the target region. Every alternatePAAD block inverts the GAM to generate a reverse attention map which guides ensuing layers to extract boundary and edge-related information, refining the segmentation process. Our experiments on three different biomedical image segmentation datasets exhibit that our PAANet achieves favorable performance when compared to other state-of-the-art methods.\&nbsp;},
	doi = {10.1109/BioSMART54244.2021.9677844},
	url = {https://ieeexplore.ieee.org/document/9677844},
	author = {Srivastava, Abhishek and Chanda, Sukalpa and Jha, Debesh and Riegler, Michael and Halvorsen, P{\r a}l and Johansen, Dag and Pal, Umapada}
}
@article {28345,
	title = {Predicting High Delays in Mobile Broadband Networks},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	month = {DEC-24-2021},
	pages = {168999 - 169013},
	publisher = { IEEE},
	doi = {10.1109/ACCESS.2021.3138695},
	url = {https://ieeexplore.ieee.org/document/9663160/http://xplorestaging.ieee.org/ielx7/6287639/6514899/09663160.pdf?arnumber=9663160},
	author = {Azza Hassan Moh {Ahmed} and Hicks, Steven and Riegler, Michael and Elmokashfi, Ahmed}
}
@article {28227,
	title = {Prediction of Cloud Fractional Cover Using Machine Learning},
	journal = {Big Data and Cognitive Computing},
	volume = {5},
	year = {2021},
	month = {Jan-12-2021},
	pages = {62},
	publisher = {MDPI},
	doi = {10.3390/bdcc5040062},
	url = {https://www.mdpi.com/2504-2289/5/4/62https://www.mdpi.com/2504-2289/5/4/62/pdf},
	author = {Svennevik, Hanna and Riegler, Michael and Hicks, Steven and Storelvmo, Trude and Hugo Lewi {Hammer}}
}
@article {27773,
	title = {Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	pages = {40496-40510},
	publisher = { IEEE},
	abstract = {Computer-aided detection, localization, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer vision methods that can be applied to polyp datasets. Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks. Furthermore, it ensures that the produced results in the community are reproducible and provide a fair comparison of developed methods. In this paper, we benchmark several recent state-of-the-art methods using Kvasir-SEG, an open-access dataset of colonoscopy images for polyp detection, localization, and segmentation evaluating both method accuracy and speed. Whilst, most methods in literature have competitive performance over accuracy, we show that the proposed ColonSegNet achieved a better trade-off between an average precision of 0.8000 and mean IoU of 0.8100, and the fastest speed of 180 frames per second for the detection and localization task. Likewise, the proposed ColonSegNetachieved a competitive dice coefficient of 0.8206 and the best average speed of 182.38 frames per second for the segmentation task. Our comprehensive comparison with various state-of-the-art methods reveals the importance of benchmarking the deep learning methods for automated real-time polyp identification and delineations that can potentially transform current clinical practices and minimize miss-detection rates.},
	keywords = {Medical image segmentation,  ColonSegNet, colonoscopy, polyps, deep learning, detection, localization, benchmarking, Kvasir-SEG},
	doi = {10.1109/ACCESS.2021.3063716},
	author = {Jha, Debesh and Ali, Sharib and Nikhil Kumar {Tomar} and H{\r a}vard D {Johansen} and Johansen, Dag and Rittscher, Jens and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {28230,
	title = {Sustainable Commercial Fishing: Digital Inspectors to the Rescue},
	journal = {Arctic Frontiers},
	year = {2021},
	publisher = {Arctic Frontiers},
	author = {Johansen, Dag and Bj{\o}rn Aslak {Juliussen} and Tor-Arne Schmidt {Nordmo} and Aril Bernhard {Ovesen} and Halvorsen, P{\r a}l and H{\r a}vard Dagenborg {Johansen} and Riegler, Michael and Jon Petter {Rui}}
}
@inproceedings {28225,
	title = {Temperature Forecasting using Tower Networks},
	journal = {Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval},
	year = {2021},
	month = {08/2021},
	pages = {18-23},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	isbn = {9781450385299},
	doi = {10.1145/346394410.1145/3463944.3469099},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3463944https://dl.acm.org/doi/10.1145/3463944.3469099https://dl.acm.org/doi/pdf/10.1145/3463944.3469099},
	author = {Siri S. {Eide} and Riegler, Michael and Hugo Lewi {Hammer} and John Bj{\o}rnar {Bremnes}},
	editor = {Dao, Minh-Son and Dang-Nguyen, Duc-Tien and Riegler, Michael}
}
@article {28232,
	title = {Towards better explainable deep learning models for embryo selection in ART},
	journal = {Human Reproduction},
	volume = {36},
	year = {2021},
	month = {Jun-08-2021},
	publisher = {Oxford Academic},
	issn = {0268-1161},
	doi = {10.1093/humrep/deab130.259},
	url = {https://academic.oup.com/humrep/article/doi/10.1093/humrep/deab130.259},
	author = {Sharma, A and Haugen, T and Hugo Lewi {Hammer} and Riegler, Michael and Stensen, M}
}
@inproceedings {42518,
	title = {Transfer Learning in Polyp and Endoscopic Tool Segmentation from Colonoscopy Images},
	journal = {Nordic Machine Intelligence},
	volume = {1},
	year = {2021},
	month = {11/2021},
	pages = {32 - 34},
	publisher = {Nordic Machine Intelligence},
	edition = {1},
	abstract = {Colorectal cancer is one of the deadliest and most widespread types of cancer in the world. Colonoscopy is the procedure used to detect and diagnose polyps from the colon, but today{\textquoteright}s detection rate shows a significant error rate that affects diagnosis and treatment. An automatic image segmentation algorithm may help doctors to improve the detection rate of pathological polyps in the colon. Furthermore, segmenting endoscopic tools in images taken during colonoscopy may contribute towards robotic assisted surgery. In this study, we trained and validated both pre-trained and not pre-trained segmentation models on two different data sets, containing images of polyps and endoscopic tools. Finally, we applied the models on two separate test sets and the best polyp model got a dice score 0.857 and the test instrument model got a dice score 0.948. Moreover, we found that pre-training of the models increased the performance in segmenting polyps and endoscopic tools.},
	keywords = {convolutional neural networks, Hyperkvasir, MedAI challenge, Polyp segmentation, Transfer Learning},
	issn = {2703-9196},
	doi = {10.5617/nmi.9132},
	url = {https://journals.uio.no/NMI/article/view/9132},
	author = {Nefeli PanagiotaN {TzavaraTzavara} and Singstad, Bj{\o}rn-Jostein},
	editor = {Riegler, Michael and Hicks, Steven and Halvorsen, P{\r a}l and Thambawita, Vajira}
}
@article {28061,
	title = {Unraveling the Impact of Land Cover Changes on Climate using Machine Learning and Explainable Artificial Intelligence},
	journal = {Big Data and Cognitive Computing},
	volume = {5},
	number = {4},
	year = {2021},
	pages = {55},
	publisher = {Multidisciplinary Digital Publishing Institute},
	abstract = {A general issue in climate science is to handle big data and run complex and computationally heavy simulations. In this paper, we explore the potential of using machine learning (ML) to spare computational time and optimize data usage. The paper analyzes the effects of changes in land cover (LC), such as deforestation or urbanization, on local climate. Along with green house gas emission, LC changes are known to be important causes of climate change. ML methods were trained to learn the relation between LC changes and temperature changes. The results showed that Random Forest (RF) outperformed other ML methods. Explainable Artificial Intelligence (XAI) was further used to interpret the RF method and explain the impact of different LC changes on temperature. The results mainly agree with the climate science literature, demonstrating that ML methods in combination with XAI can be useful in analyzing the climate effects of LC changes. All parts of the analysis pipeline are explained including data pre-processing, feature extraction, ML training, performance evaluation and XAI.},
	author = {Kolevatova, Anastasiia and Riegler, Michael and Cherubini, Francesco and Hu, Xiangping and Hugo Lewi {Hammer}}
}
@article {28007,
	title = {Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events},
	journal = {International Journal of Semantic Computing},
	volume = {15},
	number = {2},
	year = {2021},
	month = {Jan-06-2021},
	pages = {161 - 187},
	publisher = {World Scientific},
	abstract = {Developing systems for the automatic detection of events in video is a task which has gained attention in many areas including sports. More specifically, event detection for soccer videos has been studied widely in the literature. However, there are still a number of shortcomings in the state-of-the-art such as high latency, making it challenging to operate at the live edge. In this paper, we present an algorithm to detect events in soccer videos in real time, using 3D convolutional neural networks. We test our algorithm on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.},
	keywords = {3d CNN, classification, Detection, soccer events, spotting},
	issn = {1793-351X},
	doi = {10.1142/S1793351X2140002X},
	url = {https://www.worldscientific.com/doi/abs/10.1142/S1793351X2140002X},
	author = {Olav A. Nerg{\r a}r {Rongved} and Hicks, Steven and Thambawita, Vajira and H{\r a}kon Kvale {Stensland} and Zouganeli, Evi and Johansen, Dag and Midoglu, Cise and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {42767,
	title = {Visual Sentiment Analysis: A Natural Disaster Use-case Task at MediaEval 2021},
	journal = {MediaEval 2021},
	year = {2021},
	publisher = {Ceurws},
	author = {Syed Zohaib {Hassan} and Ahmad, Kashif and Riegler, Michael and Hicks, Steven and Conci, Nicola and Halvorsen, P{\r a}l and Al-Fuqaha, Ala}
}
@inproceedings {10.1145/3394171.3416287,
	title = {ACM Multimedia BioMedia 2020 Grand Challenge Overview},
	journal = {Proceedings of the 28th ACM International Conference on Multimedia},
	year = {2020},
	pages = {4655{\textendash}4658},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	abstract = {The BioMedia 2020 ACM Multimedia Grand Challenge is the second in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year{\textquoteright}s challenge, participants are asked to develop algorithms that automatically predict the quality of a given human semen sample using a combination of visual, patient-related, and laboratory-analysis-related data. Compared to last year{\textquoteright}s challenge, participants are provided with a fully multimodal dataset (videos, analysis data, study participant data) from the field of assisted human reproduction. The tasks encourage the use of the different modalities contained within the dataset and finding smart ways of how they may be combined to further improve prediction accuracy. For example, using only video data or combining video data and patient-related data. The ground truth was developed through a preliminary analysis done by medical experts following the World Health Organization{\textquoteright}s standard for semen quality assessment. The task lays the basis for automatic, real-time support systems for artificial reproduction. We hope that this challenge motivates multimedia researchers to explore more medical-related applications and use their vast knowledge to make a real impact on people{\textquoteright}s lives.},
	keywords = {artificial intelligence, Machine learning, male fertility, semen analysis, spermatozoa},
	isbn = {9781450379885},
	doi = {10.1145/3394171.3416287},
	url = {https://doi.org/10.1145/3394171.3416287},
	author = {Hicks, Steven and Thambawita, Vajira and Hugo Lewi {Hammer} and Trine B. {Haugen} and Jorunn M. {Andersen} and Witczak, Oliwia and Halvorsen, P{\r a}l and Riegler, Michael}
}
@article {27595,
	title = {Applying machine learning in motor activity time series of depressed bipolar and unipolar patients compared to healthy controls},
	journal = {PLOS ONE},
	volume = {15},
	year = {2020},
	month = {Dec-08-2021},
	pages = {e0231995},
	publisher = {PLOS ONE},
	doi = {https://doi.org/10.1371/journal.pone.0231995},
	url = {https://dx.plos.org/10.1371/journal.pone.0231995},
	author = {Jakobsen, Petter and Garcia-Ceja, Enrique and Riegler, Michael and Lena Antonsen {Stabell} and Nordgreen, Tine and Torresen, Jim and Ole Bernt {Fasmer} and Ketil Joachim {Oedegaard}},
	editor = {Na, Kyoung-Sae}
}
@misc {27332,
	title = {Big data is not always better {\textendash} prediction of live birth using machine learning on time-lapse videos of human embryos},
	year = {2020},
	publisher = {ESHRE},
	address = {ESHRE virtual 36th Annual Meeting},
	author = {Hicks, Steven and Trine B. {Haugen} and Iliceto, Mario and Hugo Lewi {Hammer} and Jorunn M. {Andersen} and Witczak, Oliwia and Riegler, Michael and Stensen, Mette}
}
@article {27638,
	title = {Comparative validation of multi-instance instrument segmentation in endoscopy: results of the ROBUST-MIS 2019 challenge},
	journal = {Medical Image Analysis},
	year = {2020},
	month = {11/2020},
	publisher = {Elsevier},
	abstract = {Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting,\&nbsp; segmenting and tracking of medical instruments based on endoscopic video im-ages have been proposed in the literature, key limitations remain to be addressed:\&nbsp; Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts).\&nbsp; Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In\&nbsp;an\&nbsp;effort\&nbsp;to\&nbsp;promote\&nbsp;solutions\&nbsp;for\&nbsp;these\&nbsp;limitations,\&nbsp; we\&nbsp;organized\&nbsp;the Robust\&nbsp; Medical\&nbsp; Instrument\&nbsp; Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms.\&nbsp; For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation.\&nbsp; The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts).\&nbsp;},
	author = {Ro{\ss}, Tobias and Reinke, Annika and Peter M. {Full} and Wagner, Martin and Kenngott, Hannes and Apitz, Martin and Hempe, Hellena and Diana Mindroc {Filimon} and Scholz, Patrick and Thuy Nuong {Tran} and Bruno, Pierangela and Pablo Arbel ́ {aez} and Bin Bian, Gui- and Bodenstedt, Sebastian and Jon Lindstrom {Bolmgren} and Laura Bravo-S ́ {anchez} and Bin Chen, Hua- and Cristina Gonz ́ {alez} and Guo, Dong and Halvorsen, P{\r a}l and Heng, Pheng-Ann and Hosgor, Enes and Hou, Zeng-Guang and Isensee, Fabian and Jha, Debesh and Jiang, Tingting and Jin, Yueming and Kirtac, Kadir and Kletz, Sabrina and Leger, Stefan and Li, Zhixuan and Klaus H. {Maier-Hein} and Ni, Zhen-Liang and Riegler, Michael and Schoeffmann, Klaus and Shi, Ruohua and Speidel, Stefanie and Stenzel, Michael and Twick, Isabell and Wang, Gutai and Wang, iacheng and Wang, Liansheng and Wang, Lu and Zhang, Yujie and Zhou, Yan-Jie and Zhu, Lei and Wiesenfarth, Manuel and Kopp-Schneider, Annette and Beat P. M ̈ {uller-Stich} and Maier-Hein, Lena}
}
@inproceedings {27324,
	title = {DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation},
	journal = {CBMS 2020: International Symposium on Computer-Based Medical Systems},
	year = {2020},
	note = {This paper was nominated for the best paper award at CBMS 2020.\&nbsp;},
	publisher = { IEEE},
	author = {Jha, Debesh and Riegler, Michael and Johansen, Dag and Halvorsen, P{\r a}l and H{\r a}vard D {Johansen}}
}
@inproceedings {27558,
	title = {The EndoTect 2020 Challenge: Evaluation and Comparison of Classification, Segmentation and Inference Time for Endoscopy},
	journal = {25th International Conference on Pattern Recognition (ICPR)},
	year = {2020},
	publisher = {IEEE},
	abstract = {The EndoTect challenge at the International Conference on Pattern Recognition 2020 aims to motivate the development of algorithms that aid medical experts in finding anomalies that commonly occur in the gastrointestinal tract. Using HyperKvasir, a large dataset containing images taken from several endoscopies, the participants competed in three tasks. Each task focuses on a specific requirement for making it useful in a real-world medical scenario. The tasks are (i) high classification performance in terms of prediction accuracy, (ii) efficient classification measured by the number of images classified per second, and (iii) pixel-level segmentation of specific anomalies. Hopefully, this can motivate different computer science researchers to help benchmark a crucial component of a future computer-aided diagnosis system, which in turn, could potentially save human lives.},
	author = {Hicks, Steven and Jha, Debesh and Thambawita, Vajira and Halvorsen, P{\r a}l and Hugo Lewi {Hammer} and Riegler, Michael}
}
@article {27265,
	title = {An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classification},
	journal = {ACM Transactions on Computing for Healthcare},
	volume = {1},
	number = {3},
	year = {2020},
	pages = {1-29},
	publisher = {ACM},
	abstract = {Precise and efficient automated identification of Gastrointestinal (GI) tract diseases can help doctors treat more patients and improve the rate of disease detection and identification. Currently, automatic analysis of diseases in the GI tract is a hot topic in both computer science and medical-related journals. Nevertheless, the evaluation of such an automatic analysis is often incomplete or simply wrong. Algorithms are often only tested on small and biased datasets, and cross-dataset evaluations are rarely performed. A clear understanding of evaluation metrics and machine learning models with cross datasets is crucial to bring research in the field to a new quality level. Towards this goal, we present comprehensive evaluations of five distinct machine learning models using Global Features and Deep Neural Networks that can classify 16 different key types of GI tract conditions, including pathological findings, anatomical landmarks, polyp removal conditions, and normal findings from images captured by common GI tract examination instruments. Inour evaluation, we introduce performance hexagons using six performance metrics such as recall, precision, specificity, accuracy, F1-score, and Matthews Correlation Coefficient to demonstrate how to determine the real capabilities of models rather than evaluatingthem shallowly. Furthermore, we perform cross-dataset evaluations using different datasets for training and testing. With these cross-dataset evaluations, we demonstrate the challenge of actually building a generalizable model that could be used across different hospitals. Our experiments clearly show that more sophisticated performance metrics and evaluation methods need to be applied to get reliable models rather than depending on evaluations of the splits of the same dataset, i.e., the performance metrics should always be interpreted together rather than relying on a single metric.},
	author = {Thambawita, Vajira and Jha, Debesh and Hugo Lewi {Hammer} and H{\r a}vard D {Johansen} and Johansen, Dag and Halvorsen, P{\r a}l and Riegler, Michael}
}
@article {27601,
	title = {A Genetic Attack Against Machine Learning Classifiers to Steal Biometric Actigraphy Profiles from Health Related Sensor Data},
	journal = {Journal of Medical Systems},
	volume = {44},
	year = {2020},
	month = {Jan-10-2020},
	pages = {1-11},
	publisher = { Springer},
	issn = {0148-5598},
	doi = {10.1007/s10916-020-01646-y},
	url = {http://link.springer.com/10.1007/s10916-020-01646-y},
	author = {Garcia-Ceja, Enrique and Morin, Brice and Aguilar-Rivera, Anton and Riegler, Michael}
}
@article {27594,
	title = {Human Activity Recognition from Multiple Sensors Data Using Multi-fusion Representations and CNNs},
	journal = {ACM Transactions on Multimedia Computing, Communications, and Applications},
	volume = {16},
	year = {2020},
	month = {Mar-06-2021},
	pages = {1 - 19},
	publisher = {ACM},
	address = {New York},
	issn = {1551-6857},
	doi = {10.1145/3377882},
	url = {https://dl.acm.org/doi/10.1145/3377882},
	author = {Farzan Majeed {Noori} and Riegler, Michael and Md Zia {Uddin} and Torresen, Jim}
}
@article {27472,
	title = {HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy},
	journal = {Scientific Data},
	volume = {7},
	year = {2020},
	month = {08/2020},
	pages = {1-14},
	publisher = {Springer Nature},
	abstract = {Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article presents\&nbsp;HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at B{\ae}rum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos, and represents anatomical landmarks as well as pathological and normal findings. The total number of images and video frames together is around 1 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer-assisted diagnosis systems. The\&nbsp;HyperKvasir\&nbsp;dataset can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine.},
	keywords = {dataset, GI, Machine learning},
	doi = {10.1038/s41597-020-00622-y},
	url = {http://www.nature.com/articles/s41597-020-00622-y},
	author = {Borgli, Hanna and Thambawita, Vajira and Pia H. {Smedsrud} and Hicks, Steven and Jha, Debesh and Sigrun L. {Eskeland} and Kristin Ranheim {Randel} and Pogorelov, Konstantin and Lux, Mathias and Duc Tien Dang {Nguyen} and Johansen, Dag and Griwodz, Carsten and H{\r a}kon Kvale {Stensland} and Garcia-Ceja, Enrique and Peter T. {Schmidt} and Hugo Lewi {Hammer} and Riegler, Michael and Halvorsen, P{\r a}l and de Lange, Thomas}
}
@inproceedings {27473,
	title = {ImageCLEF 2020: Multimedia Retrieval in Lifelogging, Medical, Nature, and Internet Applications},
	journal = {European Conference on Information Retrieval},
	year = {2020},
	pages = {533 - 541},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {This paper presents an overview of the 2020 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum{\textemdash}CLEF Labs 2020 in Thessaloniki, Greece. ImageCLEF is an ongoing evaluation initiative (run since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2020, the 18th edition of ImageCLEF will organize four main tasks: (i) a\&nbsp;Lifelog\&nbsp;task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (ii) a\&nbsp;Medical\&nbsp;task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data and adapted tasks, (iii) a\&nbsp;Coral\&nbsp;task about segmenting and labeling collections of coral images for 3D modeling, and a new (iv)\&nbsp;Web user interface\&nbsp;task addressing the problems of detecting and recognizing hand drawn website UIs (User Interfaces) for generating automatic code. The strong participation, with over 235 research groups registering and 63 submitting over 359 runs for the tasks in 2019 shows an important interest in this benchmarking campaign. We expect the new tasks to attract at least as many researchers for 2020.},
	isbn = {978-3-030-45441-8},
	issn = {0302-9743},
	doi = {10.1007/978-3-030-45442-5_69},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-45442-5_69},
	author = {Ionescu, Bogdan and M{\"u}ller, Henning and P{\'e}teri, Renaud and Dang-Nguyen, Duc-Tien and Zhou, Liting and Piras, Luca and Riegler, Michael and Halvorsen, P{\r a}l and Tran, Minh-Triet and Lux, Mathias and Gurrin, Cathal and Chamberlain, Jon and Clark, Adrian and Campello, Antonio and Alba G. Seco {de Herrera} and Ben Abacha, Asma and Datla, Vivek and Sadid A. {Hasan} and Liu, Joey and Demner-Fushman, Dina and Pelka, Obioma and Christoph M. {Friedrich} and Yashin Dicente {Cid} and Kozlovski, Serge and Liauchuk, Vitali and Kovalev, Vassili and Berari, Raul and Brie, Paul and Fichou, Dimitri and Dogariu, Mihai and Liviu Daniel {Stefan} and Mihai Gabriel {Constantin}},
	editor = {Joemon M. {Jose} and Yilmaz, Emine and Magalh{\~a}es, Jo{\~a}o and Castells, Pablo and Ferro, Nicola and M{\'a}rio J. {Silva} and Martins, Fl{\'a}vio}
}
@inproceedings {39534,
	title = {Kvasir-instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy},
	journal = {International Conference on Multimedia Modeling},
	year = {2020},
	publisher = { Springer},
	abstract = {Gastrointestinal\&nbsp;pathologies are periodically screened, biopsied, and resected using surgical tools. Usually, the procedures and the treated or resected areas are not specifically tracked or analysed during or after colonoscopies. Information regarding disease borders, development,\&nbsp; amount, and size of the resected area get lost. This can lead to poor follow-up and bothersome reassessment difficulties post-treatment. To improve the current standard and also to foster more research on the topic, we have released the\&nbsp; {\textquoteleft}{\textquoteleft}Kvasir-Instrument{\textquoteright}{\textquoteright} dataset, which consists of 590\&nbsp;annotated frames containing GI\&nbsp;procedure tools such as snares, balloons, and biopsy forceps, etc. Besides the images, the dataset includes ground truth masks and bounding boxes and has been verified by two expert GI\&nbsp;endoscopists. Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development. We obtained a dice coefficient score of 0.9158\&nbsp;and a Jaccard index of 0.8578\&nbsp;using a classical U-Net architecture. A similar dice coefficient score was observed for DoubleUNet. The qualitative results showed that the model did not work for the images with specularity and the frames with multiple tools, while the best result for both methods was observed on all other types of images. Both qualitative and quantitative results show that the model performs reasonably good, but there is potential for further improvements.\&nbsp; Benchmarking using the dataset provides an opportunity for researchers to contribute to the field of automatic endoscopic diagnostic and therapeutic tool segmentation for GI\&nbsp;endoscopy.},
	keywords = {Benchmarking, Convolutional  neural network, Gastrointestinal endoscopy, Tool segmentation},
	url = {https://www.springerprofessional.de/en/kvasir-instrument-diagnostic-and-therapeutic-tool-segmentation-d/18788368},
	author = {Jha, Debesh and Ali, Sharib and Emanuelsen, Krister and Hicks, Steven and Thambawita, Vajira and Garcia-Ceja, Enrique and Riegler, Michael and de Lange, Thomas and Peter T. {Schmidt} and H{\r a}vard D {Johansen} and Johansen, Dag and Halvorsen, P{\r a}l}
}
@inproceedings {26811,
	title = {Kvasir-SEG: A Segmented Polyp Dataset},
	journal = {International Conference on Multimedia Modeling},
	year = {2020},
	pages = {451-462},
	publisher = {Springer},
	address = {Daejeon, Korea},
	abstract = {Pixel-wise image segmentation is a highly demanding task in medical image analysis. In practice, it is difficult to find annotated medical images with corresponding segmentation masks. In this paper, we present\&nbsp; Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated and verified by an experienced gastroenterologist. We demonstrate the use of our dataset with a traditional segmentation approach and a modern deep learning based CNN approach. This work will be valuable for researchers to reproduce results and compare their methods in the future.\&nbsp;By adding segmentation masks to the Kvasir dataset, which until today only consisted of framewise annotations, we enable multimedia and computer vision researchers to contribute in the field of polyp segmentation and automatic analysis of colonoscopy videos.\&nbsp;\&nbsp;},
	keywords = {Kvasir-SEG dataset, Medical images, Polyp segmentation, ResUNet  Fuzzy c-mean clustering, Semantic segmentation},
	author = {Jha, Debesh and Pia H. {Smedsrud} and Riegler, Michael and Halvorsen, P{\r a}l and H{\r a}vard D. {Johansen} and de Lange, Thomas and Johansen, Dag}
}
@inproceedings {27639,
	title = {LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification},
	journal = {The Joint International Conference PDCAT-PAAP 2020},
	year = {2020},
	publisher = { Springer},
	abstract = {Deep Neural Networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks. A key drawback of DNNs is that the training phase can be very computationally expensive. Organizations or individuals that cannot afford purchasing state-of-the-art hardware or tapping into cloud-hosted infrastructures may face a long waiting time before the training completes or might not be able to train a model at all. Investigating novel ways to reduce the training time could be a potential solution to alleviate this drawback, and thus enabling more rapid development of new algorithms and models. In this paper, we propose LightLayers, a method for reducing the number of trainable parameters in deep neural networks (DNN). The proposed LightLayers consists of LightDense andLightConv2D layer that are as efficient as regular Conv2D and Dense layers, but uses less parameters. We resort to Matrix Factorization to reduce the complexity of the DNN models resulting into lightweight DNNmodels that require less computational power, without much loss in the accuracy. We have tested LightLayers on MNIST, Fashion MNIST, CI-FAR 10, and CIFAR 100 datasets. Promising results are obtained for MNIST, Fashion MNIST, CIFAR-10 datasets whereas CIFAR 100 shows acceptable performance by using fewer parameters.},
	keywords = {CIFAR-10, Convolutional  neural network, Deep  Learning, Fashion MNIST, Lightweight  model, MNIST, Weight decomposition},
	author = {Jha, Debesh and Yazidi, Anis and Riegler, Michael and Johansen, Dag and H{\r a}vard D. {Johansen} and Halvorsen, P{\r a}l}
}
@article {kousias2020long,
	title = {Long short term memory networks for bandwidth forecasting in mobile broadband networks under mobility},
	journal = {arXiv preprint arXiv:2011.10563},
	year = {2020},
	publisher = {arXiv},
	author = {Kousias, Konstantinos and Pappas, Apostolos and Alay, {\"O}zg{\"u} and Argyriou, Antonios and Riegler, Michael}
}
@inproceedings {27637,
	title = {Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation},
	journal = {Medico MediaEval 2020},
	year = {2020},
	publisher = {CEUR},
	abstract = {Colorectal cancer is the third most common cause of cancer worldwide. According to Global cancer statistics 2018, the incidence of colorectal cancer is increasing in both developing and developed countries. Early detection of colon anomalies such as polyps is important for cancer prevention, and automatic polyp segmentation can play a crucial role for this. Regardless of the recent advancement in early detection and treatment options, the estimated polyp miss rate is still around 20\%. Support via an automated computer-aided diagnosis system could be one of the potential solutions for the overlooked polyps. Such detection systems can help low-cost design solutions and save doctors time, which they could for example use to perform more patient examinations. In this paper, we introduce the 2020 Medico challenge, provide some information on related work and the dataset, describe the task and evaluation metrics, and discuss the necessity of organizing the Medico challenge.},
	author = {Jha, Debesh and Hicks, Steven and Emanuelsen, Krister and H{\r a}vard D. {Johansen} and Johansen, Dag and de Lange, Thomas and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {leorganiser,
	title = {Organiser Team at ImageCLEFlifelog 2020: A Baseline Approach for Moment Retrieval and Athlete Performance Prediction using Lifelog Data},
	journal = {CLEF2020},
	year = {2020},
	publisher = {CEUR Workshop Proceedings},
	author = {Le, Tu-Khiem and Ninh, Van-Tu and Zhou, Liting and Nguyen-Ngoc, Minh-Huy and Trinh, Huu-Duc and Tran, Nguyen-Hien and Piras, Luca and Riegler, Michael and Halvorsen, P{\r a}l and Lux, Mathias}
}
@inproceedings {ninh2020overview,
	title = {Overview of ImageCLEF lifelog 2020: lifelog moment retrieval and sport performance lifelog},
	journal = {CLEF2020 },
	year = {2020},
	publisher = {CEUR Workshop Proceedings},
	author = {Ninh, Van-Tu and Le, Tu-Khiem and Zhou, Liting and Piras, Luca and Riegler, Michael and Halvorsen, P{\r a}l and Tran, Minh-Triet and Lux, Mathias and Gurrin, Cathal and Dang-Nguyen, Duc-Tien}
}
@inproceedings {27644,
	title = {Overview of the ImageCLEF 2020: Multimedia Retrieval in Medical, Lifelogging, Nature, and Internet Applications},
	journal = {CLEF 2020},
	volume = {12260},
	year = {2020},
	pages = {311 - 341},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {This paper presents an overview of the 2020 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum{\textemdash}CLEF Labs 2020 in Thessaloniki, Greece. ImageCLEF is an ongoing evaluation initiative (run since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2020, the 18th edition of ImageCLEF will organize four main tasks: (i) a\&nbsp;Lifelog\&nbsp;task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (ii) a\&nbsp;Medical\&nbsp;task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data and adapted tasks, (iii) a\&nbsp;Coral\&nbsp;task about segmenting and labeling collections of coral images for 3D modeling, and a new (iv)\&nbsp;Web user interface\&nbsp;task addressing the problems of detecting and recognizing hand drawn website UIs (User Interfaces) for generating automatic code. The strong participation, with over 235 research groups registering and 63 submitting over 359 runs for the tasks in 2019 shows an important interest in this benchmarking campaign. We expect the new tasks to attract at least as many researchers for 2020.},
	isbn = {978-3-030-58218-0},
	issn = {0302-9743},
	doi = {10.1007/978-3-030-58219-710.1007/978-3-030-58219-7_22},
	url = {https://doi.org/10.1007/978-3-030-58219-7_22},
	author = {Ionescu, Bogdan and M{\"u}ller, Henning and P{\'e}teri, Renaud and Ben Abacha, Asma and Datla, Vivek and Sadid A. {Hasan} and Demner-Fushman, Dina and Kozlovski, Serge and Liauchuk, Vitali and Yashin Dicente {Cid} and Kovalev, Vassili and Pelka, Obioma and Christoph M. {Friedrich} and Alba Garc{\'\i}a Se {de Herrera} and Ninh, Van-Tu and Le, Tu-Khiem and Zhou, Liting and Piras, Luca and Riegler, Michael and Halvorsen, P{\r a}l and Tran, Minh-Triet and Lux, Mathias and Gurrin, Cathal and Dang-Nguyen, Duc-Tien and Chamberlain, Jon and Clark, Adrian and Campello, Antonio and Fichou, Dimitri and Berari, Raul and Brie, Paul and Dogariu, Mihai and Liviu Daniel {Stefan} and Mihai Gabriel {Constantin}},
	editor = {Arampatzis, Avi and Kanoulas, Evangelos and Tsikrika, Theodora and Vrochidis, Stefanos and Joho, Hideo and Lioma, Christina and Eickhoff, Carsten and N{\'e}v{\'e}ol, Aur{\'e}lie and Cappellato, Linda and Ferro, Nicola}
}
@inproceedings {27593,
	title = {PCC arena: a benchmark platform for point cloud compression algorithms},
	journal = {MMSys {\textquoteright}20: 11th ACM Multimedia Systems ConferenceProceedings of the 12th ACM International Workshop on Immersive Mixed and Virtual Environment Systems},
	year = {2020},
	month = {06/2020},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {9781450379472},
	doi = {10.1145/338629310.1145/3386293.3397112},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3386293https://dl.acm.org/doi/10.1145/3386293.3397112},
	author = {Wu, Cheng-Hao and Hsu, Chih-Fan and Kuo, Ting-Chun and Griwodz, Carsten and Riegler, Michael and Morin, G{\'e}raldine and Hsu, Cheng-Hsin},
	editor = {Eg, Ragnhild and van der Hooft, Jeroen and Raaen, Kjetil}
}
@inproceedings {27333,
	title = {PMData: a sports logging dataset},
	journal = {Proceedings of the 11th ACM Multimedia Systems Conference},
	year = {2020},
	pages = {231-236},
	publisher = {ACM},
	author = {Thambawita, Vajira and Hicks, Steven and Borgli, Hanna and H{\r a}kon Kvale {Stensland} and Jha, Debesh and Martin Kristoffer {Svensen} and Pettersen, Svein-Arne and Johansen, Dag and H{\r a}vard Dagenborg {Johansen} and Susann Dahl {Pettersen} and Nordvang, Simon and Pedersen, Sigurd and Gjerdrum, Anders and Gr{\o}nli, Tor-Morten and Per Morten {Fredriksen} and Eg, Ragnhild and Hansen, Kjeld and Fagernes, Siri and Claudi, Christine and Bi{\o}rn-Hansen, Andreas and Duc Tien Dang {Nguyen} and Kupka, Tomas and Hugo Lewi {Hammer} and Jain, Ramesh and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {27596,
	title = {PSYKOSE: A Motor Activity Database of Patients with Schizophrenia},
	journal = {2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)},
	year = {2020},
	publisher = {IEEE},
	address = {Rochester, MN, USA},
	doi = {10.1109/CBMS49503.202010.1109/CBMS49503.2020.00064},
	url = {https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9169740https://ieeexplore.ieee.org/document/9182896/http://xplorestaging.ieee.org/ielx7/9169740/9182790/09182896.pdf?arnumber=9182896},
	author = {Jakobsen, Petter and Garcia-Ceja, Enrique and Lena Antonsen {Stabell} and Ketil Joachim {Oedegaard} and Jan Oystein {Berle} and Thambawita, Vajira and Hicks, Steven and Halvorsen, P{\r a}l and Ole Bernt {Fasmer} and Riegler, Michael}
}
@inproceedings {27575,
	title = {Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus},
	journal = {MediaEval 2020},
	year = {2020},
	publisher = {CEUR},
	abstract = {Segmentation of findings in the gastrointestinal tract is a challenging but also an important task which is an important building stone for sufficient automatic decision support systems. In this work, we present our solution for the Medico 2020 task, which focused on the problem of colon polyp segmentation. We present our simple but efficient idea of using an augmentation method that uses grids in a pyramid-like manner (large to small) for segmentation. Our results show that the proposed methods work as indented and can also lead to comparable results when competing with other methods.},
	author = {Thambawita, Vajira and Hicks, Steven and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {27557,
	title = {Real-Time Detection of Events in Soccer Videos using 3D Convolutional Neural Networks},
	journal = {2020 IEEE International Symposium on Multimedia (ISM)},
	year = {2020},
	publisher = {IEEE},
	abstract = {In this paper, we present an algorithm for automatically detecting events in soccer videos using 3D convolutional neural networks. The algorithm uses a sliding window approach to scan over a given video to detect events such as goals, yellow/red cards, and player substitutions. We test the method on three different datasets from SoccerNet, the Swedish\&nbsp; Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.\&nbsp;},
	doi = {10.1109/ISM.2020.00030},
	author = {Olav A. Norg{\r a}r {Rongved} and Hicks, Steven and Thambawita, Vajira and H{\r a}kon Kvale {Stensland} and Zouganeli, Evi and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {27597,
	title = {Reproducibility Companion Paper: Instance of Interest Detection},
	journal = {Proceedings of the 28th ACM International Conference on Multimedia},
	year = {2020},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {9781450379885},
	doi = {10.1145/339417110.1145/3394171.3414811},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3394171https://dl.acm.org/doi/10.1145/3394171.3414811https://dl.acm.org/doi/pdf/10.1145/3394171.3414811},
	author = {Chang Wen {Chen} and Cucchiara, Rita and Hua, Xian-Sheng and Qi, Guo-Jun and Ricci, Elisa and Zhang, Zhengyou and Zimmermann, Roger and Yu, Fan and Wang, Dandan and Wang, Haonan and Ren, Tongwei and Tang, Jinhui and Wu, Gangshan and Chen, Jingjing and Riegler, Michael}
}
@inproceedings {27598,
	title = {Reproducibility Companion Paper: Selective Deep Convolutional Features for Image Retrieval},
	journal = {Proceedings of the 28th ACM International Conference on Multimedia},
	year = {2020},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {9781450379885},
	doi = {10.1145/339417110.1145/3394171.3414814},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3394171https://dl.acm.org/doi/10.1145/3394171.3414814https://dl.acm.org/doi/pdf/10.1145/3394171.3414814},
	author = {Chang Wen {Chen} and Cucchiara, Rita and Hua, Xian-Sheng and Qi, Guo-Jun and Ricci, Elisa and Zhang, Zhengyou and Zimmermann, Roger and Hoang, Tuan and Do, Thanh-Toan and Cheung, Ngai-Man and Riegler, Michael and Zah{\'a}lka, Jan}
}
@inproceedings {27612,
	title = {Scalable Infrastructure for Efficient Real-Time Sports Analytics},
	journal = {Companion Publication of the 2020 International Conference on Multimodal Interaction},
	year = {2020},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {Recent technological advances are adapted in sports to improve performance, avoid injuries, and make advantageous decisions. In this paper, we describe our ongoing efforts to develop and deploy PMSys, our smartphone-based athlete monitoring and reporting system. We describe our first attempts to gain insight into some of the data we have collected. Experiences so far are promising, both on the technical side and for athlete performance development. Our initial application of artificial-intelligence methods for prediction is encouraging and indicative.},
	keywords = {algorithmic analysis, artificial intelligence, Machine learning, privacy-preserving data collection, Sports performance logging},
	isbn = {9781450380027},
	doi = {10.1145/339503510.1145/3395035.3425300},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3395035https://dl.acm.org/doi/10.1145/3395035.3425300https://dl.acm.org/doi/pdf/10.1145/3395035.3425300},
	author = {H{\r a}vard D. {Johansen} and Johansen, Dag and Kupka, Tomas and Riegler, Michael and Halvorsen, P{\r a}l},
	editor = {Truong, Khiet and Heylen, Dirk and Czerwinski, Mary and Berthouze, Nadia and Chetouani, Mohamed and Nakano, Mikio}
}
@inproceedings {27331,
	title = {Toadstool: A Dataset for Training Emotional IntelligentMachines Playing Super Mario Bros},
	journal = {The ACM Multimedia Systems Conference (MMSys)},
	year = {2020},
	publisher = {ACM},
	address = {The ACM Multimedia Systems Conference (MMSys)},
	abstract = {Games are often defined as engines of experience, and they are heavily relying on emotions, they arouse in players. In this paper, we present a dataset called Toadstool\&nbsp;as well as a reproducible methodology to extend on the dataset. The dataset consists of video, sensor, and demographic data collected from ten participants playing Super Mario Bros, an iconic and famous video game. The sensor data is collected through an Empatica E4 wristband, which provides high-quality measurements and is graded as a medical device. In addition to the dataset and the methodology for data collection, we present a set of baseline experiments which show that we can use video game frames together with the facial expressions to predict the blood volume pulse of the person playing Super Mario Bros. With the dataset and the collection methodology we aim to contribute to research on emotionally aware machine learning algorithms, focusing on reinforcement learning and multimodal data fusion. We believe that the presented dataset can be interesting for a manifold of researchers to explore exciting new interdisciplinary questions.\&nbsp;},
	doi = {10.1145/3339825.3394939},
	url = {https://dl.acm.org/doi/10.1145/3339825.3394939},
	author = {Svoren, Henrik and Thambawita, Vajira and Halvorsen, P{\r a}l and Jakobsen, Petter and Garcia-Ceja, Enrique and Farzan Majeed {Noori} and Hugo Lewi {Hammer} and Lux, Mathias and Riegler, Michael and Hicks, Steven}
}
@inproceedings {27576,
	title = {Vid2Pix - A Framework for Generating High-Quality Synthetic Videos},
	journal = {2020 IEEE International Symposium on Multimedia (ISM)},
	year = {2020},
	publisher = {IEEE},
	abstract = {Data is arguably the most important resource today as it fuels the algorithms powering services we use every day. However, in fields like medicine, publicly available datasets are few, and labeling medical datasets require tedious efforts from trained specialists. Generated synthetic data can be to future successful healthcare clinical intelligence. Here, we present a GAN-based video generator demonstrating promising results.},
	author = {Oda O. {Nedrejord} and Thambawita, Vajira and Hicks, Steven and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {26858,
	title = {ACM Multimedia BioMedia 2019 Grand Challenge Overview},
	journal = {The ACM International Conference on Multimedia (ACM MM)},
	year = {2019},
	month = {10/2019},
	pages = {2563-2567},
	publisher = {ACM Press},
	address = {New York, New York, USA},
	abstract = {The BioMedia 2019 ACM Multimedia Grand Challenge is the first in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year{\textquoteright}s challenge, the participants are asked to develop efficient algorithms which automatically detect a variety of findings commonly identified in the gastrointestinal (GI) tract (a part of the human digestive system). The purpose of this task is to develop methods to aid medical doctors performing routine endoscopy inspections of the GI tract. In this paper, we give a detailed description of the four different tasks of this year{\textquoteright}s challenge, present the datasets used for training and testing, and discuss how each submission is evaluated both qualitatively and quantitatively.},
	isbn = {9781450368896},
	doi = {10.1145/334303110.1145/3343031.3356058},
	url = {http://dl.acm.org/citation.cfm?doid=3343031http://dl.acm.org/citation.cfm?doid=3343031.3356058http://dl.acm.org/ft_gateway.cfm?id=3356058\&ftid=2092095\&dwn=1},
	author = {Hicks, Steven and Petlund, Andreas and de Lange, Thomas and Peter Thelin {Schmidt} and Halvorsen, P{\r a}l and Riegler, Michael and Pia H. {Smedsrud} and Trine B. {Haugen} and Kristin Ranheim {Randel} and Pogorelov, Konstantin and H{\r a}kon Kvale {Stensland} and Dang-Nguyen, Duc-Tien and Lux, Mathias},
	editor = {Larson, Martha and Gravier, Guillaume and Hung, Hayley and Ngo, Chong-Wah and Wei Tsang {Ooi}}
}
@article {riegler2019artificial,
	title = {Artificial intelligence as a tool in predicting sperm motility and morphology: P-116},
	journal = {Human Reproduction},
	volume = {34},
	year = {2019},
	publisher = {Oxford Academic},
	author = {Riegler, Michael and Andersen, J and Andersen, Marie and Hugo Lewi {Hammer} and Hicks, Steven and Hicks, Alexander and Oliwia, W and Haugen, T}
}
@article {witczak2019artificial,
	title = {Artificial intelligence predicts sperm motility from sperm fatty acids: P-120},
	journal = {Human Reproduction},
	volume = {34},
	year = {2019},
	publisher = {Oxford Academic},
	author = {Witczak, O and Andersen, J and Hicks, Steven and Hugo Lewi {Hammer} and Riegler, Michael and Haugen, T}
}
@article {26455,
	title = {Automatic detection of passable roads after floods in remote sensed and social media data},
	journal = {Signal Processing: Image Communication},
	volume = {74},
	year = {2019},
	pages = {110-118},
	publisher = {Elsevier},
	abstract = {This paper addresses the problem of floods classification and floods aftermath detection based onboth social media and satellite imagery. Automatic detection of disasters such as floods is still a very challenging task. The focus lies on identifying passable routes or roads during floods. Two novel solutions are presented, which were developed for two corresponding tasks at the MediaEval 2018 benchmarking challenge. The tasks are (i) identification of images providing evidence for road passability and (ii) differentiation and detection of passable and non-passable roads in images from two complementary sources of information. For the first challenge, we mainly rely on object and scene-level features extracted through multiple deep models pre-trained on the ImageNet and Places datasets. The object and scene-level features are then combined using early, late and double fusion techniques. To identify whether or not it is possible for a vehicle to pass a road in satellite images, we rely on Convolutional Neural Networks and a transfer learning-based classification approach. The evaluation of the proposed methods is carried out on the large-scale datasets provided for the benchmark competition. The results demonstrate significant improvement in the performance over the recent state-of-art approaches.},
	keywords = {convolutional neural networks, Flood detection, Multimedia Indexing and Retrieval, Natural Disasters, Satellite Imagery, Social Media},
	doi = {https://doi.org/10.1016/j.image.2019.02.002},
	author = {Ahmad, Kashif and Pogorelov, Konstantin and Riegler, Michael and Ostroukhova, Olga and Halvorsen, P{\r a}l and Conci, Nicola and Dahyot, Rozenn}
}
@inproceedings {borgli19ISMICT,
	title = {Automatic Hyperparameter Optimization for Transfer Learning on Medical Image Datasets Using Bayesian Optimization},
	journal = {13th International Symposium on Medical Information and Communication Technology (ISMICT)},
	year = {2019},
	pages = {1-6},
	publisher = {IEEE},
	doi = {10.1109/ISMICT.2019.8743779},
	author = {Borgli, Rune and H{\r a}kon Kvale {Stensland} and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {pogorelov2019bleeding,
	title = {Bleeding detection in wireless capsule endoscopy videos{\textemdash}Color versus texture features},
	journal = {Journal of applied clinical medical physics},
	volume = {20},
	year = {2019},
	pages = {141-154},
	publisher = {Wiley Online Library},
	author = {Pogorelov, Konstantin and Suman, Shipra and Fawnizu Azmadi {Hussin} and Aamir Saeed {Malik} and Ostroukhova, Olga and Riegler, Michael and Halvorsen, P{\r a}l and Shiaw Hooi {Ho} and Goh, Khean-Lee}
}
@inbook {27154,
	title = {Challenges for Multimedia Research in E-Sports Using Counter-Strike Global Offensive as an Example},
	booktitle = {Savegame},
	volume = {4},
	year = {2019},
	pages = {197 - 206},
	publisher = {Springer Fachmedien Wiesbaden},
	organization = {Springer Fachmedien Wiesbaden},
	address = {Wiesbaden},
	abstract = {That video and computer games have reached the masses is a well-known fact. However, game streaming and, therefore, watching other people play videogames has also outgrown its humble beginnings by far. Game streams, be it live or recorded, are viewed by millions. Many of the streams are broadcasting competitive multiplayer games. This is called e-sports and it is very similar to sports broadcasting. E-sports is organized in leagues and tournaments in which players can compete in controlled environments and viewers can experience the matches, discuss and criticize just like in physical sports. In this paper, we look into the challenges for computer science in general and multimedia research in particular. The multimedia research community has done a lot of work on video streaming, broadcasting and analyzing the audience, but has missed the opportunity to investigate e-sports in detail. We focus on one particular game we deem representative for e-sports,\&nbsp;Counter-Strike: Global Offensive, and investigate how the audience consumes game streams from competitive tournaments.},
	isbn = {978-3-658-27394-1},
	issn = {2524-3241},
	doi = {10.1007/978-3-658-27395-8_13},
	url = {http://link.springer.com/10.1007/978-3-658-27395-8_13},
	author = {Lux, Mathias and Riegler, Michael and Halvorsen, P{\r a}l and Dang-Nguyen, Duc-Tien and Potthast, Martin},
	editor = {Elmenreich, Wilfried and Ren{\'e} Reinhold {Schallegger} and Schniz, Felix and Gabriel, Sonja and P{\"o}lsterl, Gerhard and Wolfgang B. {Ruge}}
}
@article {gurrin2019invited,
	title = {Comparing Approaches to Interactive Lifelog Search at the Lifelog Search Challenge (LSC2018)},
	journal = {ITE Transactions on Media Technology and Applications},
	volume = {7},
	number = {2},
	year = {2019},
	pages = {46{\textendash}59},
	publisher = {The Institute of Image Information and Television Engineers},
	author = {Gurrin, Cathal and Schoeffmann, Klaus and Joho, Hideo and Leibetseder, Andreas and Zhou, Liting and Duane, Aaron and Dang-Nguyen, Duc-Tien and Riegler, Michael and Piras, Luca and ,  and Tran, Minh-Triet}
}
@article {26546,
	title = {Deep Learning for Automatic Generation of Endoscopy Reports},
	journal = {Gastrointestinal Endoscopy},
	volume = {89},
	year = {2019},
	month = {06/2019},
	publisher = {Elsevier},
	address = {Gastrointestinal Endoscopy},
	doi = {10.1016/j.gie.2019.04.053},
	author = {Hicks, Steven and Pia H. {Smedsrud} and Riegler, Michael and de Lange, Thomas and Petlund, Andreas and Sigrun Losada {Eskeland} and Pogorelov, Konstantin and Peter Thelin {Schmidt} and Halvorsen, P{\r a}l}
}
@article {jakobsen2019detecting,
	title = {The detecting and predicting mood transitions in bipolar disorder study protocol (a sub-study of the Introducing Mental Health through Adaptive Technology (INTROMAT) project)},
	journal = {Bipolar Disorders},
	volume = {21},
	year = {2019},
	pages = {68{\textendash}69},
	publisher = { Wiley},
	author = {Jakobsen, P and Fasmer, OB and Riegler, Michael and Garcia-Ceja, E and Henriksen, TEG and McInnis, TNM and Oedegaard, JTKJ}
}
@article {26631,
	title = {Efficient Live and On-Demand Tiled HEVC 360 VR Video Streaming},
	journal = {International Journal of Semantic Computing},
	volume = {13},
	number = {3},
	year = {2019},
	pages = {367-391},
	publisher = {World Scientific},
	abstract = {360\&nbsp;panorama video displayed through Virtual reality (VR) glasses or large screens o{\textregistered}ers immersive user experiences, but as such technology becomes commonplace, the need for e{\textpm}cient streaming methods of such high-bitrate videos arises. In this respect, the attention that 360panorama video has received lately is huge. Many methods have already been proposed, and in this paper, we shed more light on the di{\textregistered}erent trade-o{\textregistered}s in order to save bandwidth while preserving the video quality in the user{\textquoteright}s ̄eld-of-view (FoV). Using 360\&nbsp;VR content delivered to a Gear VR head-mounted display with a Samsung Galaxy S7 and to a Huawei Q22 set-top- box, we have tested various tiling schemes analyzing the tile layout, the tiling and encoding overheads, mechanisms for faster quality switching beyond the DASH segment boundaries and quality selection con ̄gurations. In this paper, we present an e{\textpm}cient end-to-end design and real-world implementation of such a 360\&nbsp;streaming system. Furthermore, in addition to researching an on-demand system, we also go beyond the existing on-demand solutions and present a\&nbsp;live\&nbsp;streaming system which strikes a trade-o{\textregistered} between bandwidth usage and the video quality in the user{\textquoteright}s FoV. We have created an architecture that combines RTP and DASH, and our system multiplexes a single HEVC hardware decoder to provide faster quality switching than at the traditional GOP boundaries. We demonstrate the performance and illustrate the trade-o{\textregistered}s through real-world experiments where we can report comparable bandwidth savings to existing on-demand approaches, but with faster quality switches when the FoV changes.},
	author = {Jeppsson, Mattis and Espeland, H{\r a}vard and Kupka, Tomas and Langseth, Ragnar and Petlund, Andreas and Qiaoqiao, Peng and Xue, Chuansong and Johansen, Dag and Pogorelov, Konstantin and H{\r a}kon Kvale {Stensland} and Griwodz, Carsten and Riegler, Michael and Halvorsen, P{\r a}l}
}
@misc {runeGTC2019,
	title = {Efficient Processing of Medical Videos in a Multi-auditory Environment Using Gpu Lending},
	year = {2019},
	address = {NVIDIA{\textquoteright}s GPU Technology Conference (GTC)},
	author = {Borgli, Rune and Pogorelov, Konstantin and Riegler, Michael and Markussen, Jonas and H{\r a}kon Kvale {Stensland} and Halvorsen, P{\r a}l and Griwodz, Carsten and Sigrun Losadal {Eskeland} and de Lange, Thomas}
}
@inproceedings {26519,
	title = {Estimating Downlink Throughput from End-User Measurements in Mobile Broadband Networks},
	journal = {IEEE World of Wireless, Mobile and Multimedia Networks (WoWMoM)},
	year = {2019},
	pages = {1-10},
	publisher = { IEEE},
	abstract = {In recent years, Downlink (DL) throughput estima- tion in Mobile Broadband (MBB) networks has gained immense popularity and it is expected to become a vital component of the upcoming fifth generation (5G) systems. Plentiful adaptive video streaming algorithms greatly rely on accurate DL throughput predictions to adapt their mechanisms and ensure high Quality of Service (QoS) to the end-users. Thus far, conventional DL throughput estimation approaches, also known as speed tests, require an extensive exchange of TCP traffic over the network for an allocated time duration. While such tools appear to deliver trustworthy results, they turn out to be inefficient when mobile subscriptions with limited data plans are engaged.In this paper, we propose a supervised Machine Learning (ML) solution for DL throughput estimation that aims at delivering highly accurate predictions while significantly limiting the over- the-air data consumption. We capture the network performance metrics by exploring both crowdsourced and controlled test- ing methodologies. We leverage RTR-NetTest, a platform of broadband measurements provided by the Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR), and MONROE-NetTest, its counterpart wrapper built as an Experi- ment as a Service (EaaS) on top of Measuring Mobile Broadband Networks in Europe (MONROE). Results reveal that our solution can achieve a 39.7\% reduction in terms of data consumption while delivering a Median Absolute Percentage Error (MdAPE) of 5.55\%. We further show that accuracy can be traded-off, for example, a significant data consumption reduction of 95.15\% can be achieved for a MdAPE of 20\%.},
	author = {Kousias, Konstantinos and Alay, {\"O}zg{\"u} and Argyriou, Antonios and Lutu, Andra and Riegler, Michael}
}
@inproceedings {26923,
	title = {Extracting temporal features into a spatial domain using autoencoders for sperm video analysis},
	journal = {MediaEval 2019},
	year = {2019},
	month = {10/2019},
	publisher = {CEUR Workshop Proceedings},
	abstract = {In this paper, we present a two-step deep learning method that is used to predict sperm motility and morphology based on video recordings of human spermatozoa. First, we use an autoencoder to extract temporal features from a given semen video and plot these into image-space, which we call feature-images. Second, these feature-images are used to perform transfer learning to predict the motility and morphology values of human sperm. The presented method shows it{\textquoteright}s capability to extract temporal information into spatial domain feature-images which can be used with traditional convolutional neural networks. Furthermore, the accuracy of the predicted motility of a given semen sample shows that a deep learning-based model can capture the temporal information of microscopic recordings of human semen.},
	author = {Thambawita, Vajira and Halvorsen, P{\r a}l and Hugo Lewi {Hammer} and Riegler, Michael and Trine B. {Haugen}}
}
@article {26754,
	title = {Flexible device compositions and dynamic resource sharing in PCIe interconnected clusters using Device Lending},
	journal = {Cluster Computing},
	volume = {22},
	year = {2019},
	month = {09/2019},
	pages = {1-24},
	publisher = { Springer},
	abstract = {Modern workloads often exceed the processing and I/O capabilities provided by resource virtualization, requiring direct access to the physical hardware in order to reduce latency and computing overhead. For computers interconnected in a cluster, access to remote hardware resources often requires facilitation both in hardware and specialized drivers with virtualization support. This limits the availability of resources to specific devices and drivers that are supported by the virtualization technology being used, as well as what the interconnection technology supports. For PCI Express (PCIe) clusters, we have previously proposed Device Lending as a solution for enabling direct low latency access to remote devices. The method has extremely low computing overhead and does not require any application- or device-specific distribution mechanisms. Any PCIe device, such as network cards disks, and GPUs, can easily be shared among the connected hosts. In this work, we have extended our solution with support for a virtual machine (VM) hypervisor. Physical remote devices can be {\textquotedblleft}passed through{\textquotedblright} to VM guests, enabling direct access to physical resources while still retaining the flexibility of virtualization. Additionally, we have also implemented multi-device support, enabling shortest-path peer-to-peer transfers between remote devices residing in different hosts. Our experimental results prove that multiple remote devices can be used, achieving bandwidth and latency close to native PCIe, and without requiring any additional support in device drivers. I/O intensive workloads run seamlessly using both local and remote resources. With our added VM and multi-device support, Device Lending offers highly customizable configurations of remote devices that can be dynamically reassigned and shared to optimize resource utilization, thus enabling a flexible composable I/O infrastructure for VMs as well as bare-metal machines.},
	issn = {1573-7543},
	doi = {10.1007/s10586-019-02988-0},
	url = {https://link.springer.com/article/10.1007/s10586-019-02988-0},
	author = {Markussen, Jonas and Lars Bj{\o}rlykke {Kristiansen} and Borgli, Rune and H{\r a}kon Kvale {Stensland} and Seifert, Friedrich and Riegler, Michael and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@inproceedings {noori2019fusion,
	title = {Fusion of multiple representations extracted from a single sensor{\textquoteright}s data for activity recognition using CNNs},
	journal = {2019 International Joint Conference on Neural Networks (IJCNN)},
	year = {2019},
	pages = {1{\textendash}6},
	publisher = {IEEE},
	author = {Farzan Majeed {Noori} and Garcia-Ceja, Enrique and Md Zia {Uddin} and Riegler, Michael and T{\o}rresen, Jim}
}
@inproceedings {27106,
	title = {GameStory Task at MediaEval 2019},
	journal = {Proceedings of MediaEval 2019},
	year = {2019},
	month = {10/2019},
	publisher = {CEUR Workshop Proceedings (CEUR-WS.org)},
	abstract = {Game video streams are watched by millions, so that, meanwhile, one can make a living from broadcasting and commenting video games, whereas some have become professional e-sports athletes. E-sports leagues and tournaments have emerged worldwide, where players compete in controlled environments, streaming the matches online, and allowing the audience to discuss and criticize the game- play. In the GameStory task, held for the second time at MediaEval, we foster research into this exciting domain. Our focus is on an- alyzing and summarizing video game streams. With the help of\&nbsp;ZNIPE.tv, we compiled a high-quality dataset of a\&nbsp;Counter-Strike: Global Offensive\&nbsp;tournament alongside ground truth labels for two analysis tasks, forming a basis for summarization.},
	author = {Lux, Mathias and Riegler, Michael and Dang-Nguyen, Duc-Tien and Pirker, Johanna and Potthast, Martin and Halvorsen, P{\r a}l}
}
@inproceedings {26689,
	title = {GANEx: A complete pipeline of training, inference and benchmarking GAN experiments},
	journal = {2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
	year = {2019},
	pages = {1-4},
	publisher = {IEEE},
	keywords = {GAN, GANEx, Generative Adversarial Network},
	author = {Thambawita, Vajira and Hugo Lewi {Hammer} and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {noori2019heart,
	title = {Heart Rate Prediction from Head Movement during Virtual Reality Treatment for Social Anxiety},
	journal = {2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
	year = {2019},
	pages = {1{\textendash}5},
	publisher = {IEEE},
	author = {Farzan Majeed {Noori} and Kahlon, Smiti and Lindner, Philip and Nordgreen, Tine and Torresen, Jim and Riegler, Michael}
}
@inbook {26709,
	title = {Image Retrieval Evaluation in Specific Domains},
	booktitle = {Information Retrieval Evaluation in a Changing World - Lessons Learned from 20 Years of CLEF},
	volume = {INRE, volume 41},
	year = {2019},
	pages = {275-305},
	publisher = { Springer},
	organization = { Springer},
	abstract = {Image retrieval was, and still is, a hot topic in research. It comes with many challenges that changed over the years with the emergence of more advanced methods for analysis and enormous growth of images created, shared and consumed. This chapter gives an overview of domain-specific image retrieval evaluation approaches, which were part of the ImageCLEF evaluation campaign . Specifically, the robot vision, photo retrieval, scalable image annotation and lifelogging tasks are presented. The ImageCLEF medical activity is described in a separate chapter in this volume. Some of the presented tasks have been available for several years, whereas others are quite new (like lifelogging). This mix of new and old topics has been chosen to give the reader an idea about the development and trends within image retrieval. For each of the tasks, the datasets, participants, techniques used and lessons learned are presented and discussed leading to a comprehensive summary.},
	doi = {https://doi.org/10.1007/978-3-030-22948-1_12},
	author = {Piras, Luca and Caputo, Barbara and Dang-Nguyen, Duc-Tien and Riegler, Michael and Halvorsen, P{\r a}l},
	editor = {Ferro, Nicola and Peters, Carol}
}
@inproceedings {ionescu2019imageclef,
	title = {ImageCLEF 2019: Multimedia Retrieval in Lifelogging, Medical, Nature, and Security Applications},
	journal = {European Conference on Information Retrieval},
	year = {2019},
	month = {02/2019},
	pages = {301{\textendash}308},
	publisher = {Springer},
	author = {Ionescu, Bogdan and M{\"u}ller, Henning and P{\'e}teri, Renaud and Dang-Nguyen, Duc-Tien and Piras, Luca and Riegler, Michael and Tran, Minh-Triet and Lux, Mathias and Gurrin, Cathal and Yashin Dicente {Cid}}
}
@inproceedings {ionescu2019imageclef,
	title = {ImageCLEF 2019: Multimedia retrieval in medicine, lifelogging, security and nature},
	journal = {International Conference of the Cross-Language Evaluation Forum for European Languages},
	year = {2019},
	pages = {358{\textendash}386},
	publisher = {Springer},
	address = {Cham},
	author = {Ionescu, Bogdan and M{\"u}ller, Henning and P{\'e}teri, Renaud and Yashin Dicente {Cid} and Liauchuk, Vitali and Kovalev, Vassili and Klimuk, Dzmitri and Tarasau, Aleh and Ben Abacha, Asma and Sadid A {Hasan} and Riegler, Michael}
}
@misc {morin2019interview,
	title = {An interview with G{\'e}raldine Morin},
	howpublished = {ACM SIGMultimedia Records},
	volume = {10},
	year = {2019},
	pages = {4},
	publisher = {ACM},
	author = {Morin, G{\'e}raldine and Riegler, Michael}
}
@misc {26509,
	title = {Kunstig intelligens for endoskopi {\textendash} Automatisk deteksjon av lesjoner i sanntid},
	year = {2019},
	publisher = {Norsk Gastroenterologisk Forening},
	address = {NGF Nytt, Vol. 26, No 1, March 2019, p. 34},
	abstract = {BAKGRUNN: I krysningspunktet mellom matematikk, informatikk og statistikk finner vi den vitenskapelige disiplinen kunstig intelligens (KI). Sammen med de siste {\r a}rs eksplosive utvikling innen teknologi har KI muliggjort nye algoritmer, modeller og systemer for maskinassistert diagnostikk. Resultater fra KI basert p{\r a} dype nevrale nettverk har vist spesielt stort potensiale, ogs{\r a} for automatisk deteksjon av lesjoner og anatomiske landemerker i gastrointestinaltraktus under endoskopi. Med sensitivitet og spesifisitet for deteksjon av polypper i tykktarmp{\r a} over 90\% m{\o}ter slike metoder n{\o}dvendige kliniske krav, men mange eksperimenter er utf{\o}rt p{\r a} begrensede datasett, eller analysert p{\r a} feilaktig grunnlag grunnet manglende tilgang og forst{\r a}else hos informatikere. For {\r a} oppn{\r a} best mulig resultat eret interdisiplin{\ae}rt samarbeid mellom klinikere og informatikereen forutsetning. Informatikerne trenger medisinske innspill for {\r a} lage effektive systemer som fungerer ute i klinikken, og klinikerne trenger forst{\r a}else av systemet for {\r a} kunne stole p{\r a} resultatet og stille p{\r a}litelige diagnoser. En stor utfordring for denne tilliten erat fremgangsm{\r a}ten til en KI-algoritme sees p{\r a} som en svart boks hvor ingen n{\o}yaktig kan dechiffrere hvordan systemet kom fremtil sin konklusjon.METODE: Vi har gjennom mange {\r a}r samlet en stor bilde- database fra endoskopier utf{\o}rt ved B{\ae}rum Sykehus, Vestre Viken HF. Bildene er gjennomg{\r a}tt og annotert av tre erfarne endoskop{\o}rer og fordelt p{\r a} 16 klasser, inkludert normal Z-linje, {\o}sofagitt, normal c{\o}kum, polypper og ulcer{\o}s colitt. Deretter er bildene brukt til {\r a} utvikle, trene og teste KI-modeller. Modellene er basert p{\r a} maskinl{\ae}ring og dyp l{\ae}ring, en gren innen KI. Med v{\r a}rt system Mimir, som kombinerer KI med informasjonss{\o}k og-gjenfinning, s{\o}ker vi {\r a} lage et helhetlig beslutningsst{\o}ttesystem for endoskop{\o}rer. Algoritmene analyserer videoer i sanntid, finner lesjoner, klassifiserer disse og gir skop{\o}ren live feedback om funn under unders{\o}kelsen, slik at funnene kan unders{\o}kes n{\ae}rmere. Mimir presenterer deretter resultatene i egen programvare, og bruker blant annet {\textquotedblleft}heatmaps{\textquotedblright} til {\r a} forklare hvordan konklusjonen er n{\r a}dd, og er p{\r a} den m{\r a}ten et bidrag p{\r a} veien til {\r a} forst{\r a} hvordan KI-algoritmene fungerer. Videre jobber vi med {\r a} videreutvikle Mimirs st{\o}tte for automatisk rapportgenerering, med bilderog standardtekst basert p{\r a} funn fra unders{\o}kelsen.RESULTATER: Deteksjon og klassifisering for de 16 gruppene har vist en sensitivitet p{\r a} 0,939 og en spesifisitet p{\r a} 0,996. Algoritmene v{\r a}re klarer {\r a} prosessere bildene i hastigheter p{\r a} mellom 30 - 1000 bilder per sekund, raskt nok til {\r a} kj{\o}re deteksjon i sanntid. En prototype av systemet er i samr{\r a}d med klinikere testet ved {\r a} koble til et koloskopisystem ute i klinikken, og kann{\r a} analysere videoer i sanntid.KONKLUSJON: Tester av v{\r a}re system viser at KI kan bli et viktig hjelpemiddel for {\r a} bedre oppdage GI-forandringer, og generere automatiske rapporter i l{\o}pet av n{\ae}rmeste fremtid. Dette kan fungere som viktig beslutningsst{\o}tte for endoskop{\o}rer, og kan brukes i oppl{\ae}ring av nye endoskop{\o}rer. Den st{\o}rste begrensningen med KI er at vi per i dag ikke vet hvordan systemet kommer frem til sin konklusjon, som kan p{\r a}virke i hvor stor grad vi stoler p{\r a} resultatet. Vi arbeider derfor med et helhetlig system som ikke bare hjelper legen med diagnostikk, men ogs{\r a} forklarer hvordan konklusjonen er n{\r a}dd, samt {\r a} generere automatiske rapporter fra unders{\o}kelsen.},
	author = {Pia H. {Smedsrud} and Riegler, Michael and de Lange, Thomas and Petlund, Andreas and Sigrun Losada {Eskeland} and Halvorsen, P{\r a}l}
}
@inproceedings {ninh2019lifer,
	title = {LIFER 2.0: Discovering Personal Lifelog Insights using an Interactive Lifelog Retrieval System},
	journal = {CLEF2019 Working Notes},
	year = {2019},
	publisher = {CEUR Workshop Proceedings (CEUR-WS.org)},
	author = {Ninh, Van-Tu and Le, Tu-Khiem and Zhou, Liting and Piras, Luca and Riegler, Michael and Lux, Mathias and Tran, Minh-Triet and Gurrin, Cathal and Dang-Nguyen, Duc-Tien}
}
@article {26926,
	title = {Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction},
	journal = {Nature Scientific Reports },
	volume = {9},
	number = {16770},
	year = {2019},
	publisher = {Springer Nature},
	abstract = {Methods for automatic analysis of clinical data are usually targeted towards a specific modality and do not make use of all relevant data available. In the field of male human reproduction, clinical and biological dataare not used to its fullest potential. Manual evaluation of a semen sample using a microscope is time-consuming and requires extensive training. Furthermore, the validity of manual semen analysis has been questioned due to limited reproducibility, and often high inter-personnel variation. The existing computer-aided sperm analyzer systems are not recommended for routine clinical use due to methodological challenges caused by the consistency of the semen sample. Thus, there is a need for an improved methodology.We use modern and classical machine learning techniques together with a dataset consisting of\&nbsp;85\&nbsp;videos of human semen samples and related participant data to automatically predict sperm motility. Used techniques include simple linear regression and more sophisticated methods using convolutional neural networks. Our results indicate that sperm motility prediction based on deep learning using sperm motility videos is rapid to perform and consistent. The algorithms performed worse when participant data was added. In conclusion, machine learning-based automatic analysis may become a valuable tool in male infertility investigation and research.},
	author = {Hicks, Steven and Jorunn M. {Andersen} and Witczak, Oliwia and Thambawita, Vajira and Halvorsen, P{\r a}l and Hugo Lewi {Hammer} and Trine B. {Haugen} and Riegler, Michael}
}
@article {26857,
	title = {Maskinl{\ae}ringssystemer for gastrointestinale endoskopier},
	journal = {Best Practice Nordic - Gastroenterologi},
	year = {2019},
	month = {07/2019},
	publisher = {BestPracticeNordic},
	abstract = {Assistert diagnostikk med hjelp av kunstig intelligens (KI) har v{\ae}rt etterspurt lenge og kan bli et viktig hjelpemiddel innen medisin, godt hjulpet av den raske utviklingen innen maskinvare. Denne har gjort innf{\o}ringen av slike hjelpemidler mulig p{\r a} relativt kort sikt. Sikrere p{\r a}visning og klassifisering av funn og lesjoner innen radiologi og endoskopi er i ferd med {\r a} bli et viktig forskningsomr{\r a}de innen KI, og det fokuseres spesielt p{\r a} maskinl{\ae}ring. Imidlertid krever vellykket utvikling et komplett system som kan brukes i sanntid i daglig praksis, og som begrenser seg til utvikling av algoritmer. Det kreves ogs{\r a} store randomiserte studier for {\r a} fastsl{\r a} om kvaliteten og p{\r a}liteligheten til systemene er god. Vi deler i denne artikkelen v{\r a}re erfaringer fra utviklingen av et system for gastrointestinale endoskopier\&nbsp;og belyser viktige utfordringer for {\r a} skape en effektiv digital assistent.},
	url = {https://bestprac.no/maskinlaeringssystemer-for-gastrointestinale-endoskopier/},
	author = {Riegler, Michael and Pia H. {Smedsrud} and de Lange, Thomas and Halvorsen, P{\r a}l}
}
@inproceedings {26859,
	title = {Medical Multimedia Systems and Applications},
	journal = {Proceedings of the 27th ACM International Conference on Multimedia  - MM {\textquoteright}19},
	year = {2019},
	month = {1072019},
	pages = {2711-2713},
	publisher = {ACM Press},
	address = {New York, NY, USA},
	abstract = {In recent years, we have observed a rise of interest in the multimedia community towards research topics related to health. It can be observed that this goes into two interesting directions. One is personal health with a larger focus on well-being and everyday healthy living. The other direction focuses more on multimedia challenges within the health-care systems, for example, how can multimedia content produced in hospitals be used efficiently but also on the user perspective of patients and health-care personal. Challenges and requirements in this interesting and challenging direction are similar to classic multimedia research, but with some additional pitfalls and challenges. This tutorial aims to give a general introduction to the research area; to provide an overview of specific requirements, pitfalls and challenges; to discuss existing and possible future work; and to elaborate on how machine learning approaches can help in multimedia-related challenges to improve the health-care quality for patients and support medical experts in their daily work.},
	isbn = {9781450368896},
	doi = {https://doi.org/10.1145/3343031.3351319},
	url = {http://dl.acm.org/citation.cfm?doid=3343031http://dl.acm.org/citation.cfm?doid=3343031.3351319http://dl.acm.org/ft_gateway.cfm?id=3351319\&ftid=2091902\&dwn=1},
	author = {Halvorsen, P{\r a}l and Riegler, Michael and Schoeffmann, Klaus},
	editor = {Amsaleg, Laurent and Huet, Benoit and Larson, Martha and Gravier, Guillaume and Hung, Hayley and Ngo, Chong-Wah and Wei Tsang {Ooi}}
}
@inproceedings {26931,
	title = {Medico Multimedia Task at MediaEval 2019},
	journal = {MediaEval},
	year = {2019},
	publisher = {CEUR Workshop Proceedings},
	abstract = {The Medico: Multimedia for Medicine Task\&nbsp;is running for the third time as part of MediaEval 2019. This year, we have changed the task from anomaly detection in images of the gastrointestinal tract to focus on the automatic prediction of human semen quality based on videos. The purpose of this task is to aid in the assessment of male reproductive health by providing a quick and consisted method of analyzing human semen. In this paper, we describe the task in detail, give a brief description of the provided dataset, and discuss the evaluation process and the metrics used to rank the submissions of the participants.},
	author = {Hicks, Steven and Halvorsen, P{\r a}l and Trine B. {Haugen} and Jorunn M. {Andersen} and Witczak, Oliwia and Pogorelov, Konstantin and Hugo Lewi {Hammer} and Dang-Nguyen, Duc-Tien and Lux, Mathias and Riegler, Michael}
}
@inproceedings {ahmad2019multi,
	title = {Multi-Modal Machine Learning for Flood Detection in News, Social Media and Satellite Sequences},
	journal = {Multimediaeval Benchmark 2019},
	year = {2019},
	month = {10/2019},
	publisher = {CEUR Workshop Proceedings},
	author = {Ahmad, Kashif and Pogorelov, Konstantin and Ullah, Mohib and Riegler, Michael and Conci, Nicola and Langguth, Johannes and Al-Fuqaha, Ala}
}
@article {27107,
	title = {Natural disasters detection in social media and satellite imagery: a survey},
	journal = {Multimedia Tools and Applications},
	volume = {78},
	year = {2019},
	month = {Jan-11-2019},
	pages = {31267 - 31302},
	publisher = { Springer},
	issn = {1380-7501},
	doi = {10.1007/s11042-019-07942-1},
	author = {Said, Naina and Ahmad, Kashif and Riegler, Michael and Pogorelov, Konstantin and Hassan, Laiq and Ahmad, Nasir and Conci, Nicola}
}
@inproceedings {26860,
	title = {One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression},
	journal = {Proceedings of the 4th International Workshop on Multimedia for Personal Health \& Health Care  - HealthMedia {\textquoteright}19},
	year = {2019},
	month = {10/2019},
	pages = {9-15},
	publisher = {ACM Press},
	address = {New York, NY, USA},
	abstract = {Nowadays, it has become possible to measure different human activities using wearable devices. Besides measuring the number of daily steps or calories burned, these datasets have much more potential since different activity levels are also collected. Such data would be helpful in the field of psychology because it can relate to various mental health issues such as changes in mood and stress. In this paper, we present a machine learning approach to detect depression using a dataset with motor activity recordings of one group of people with depression and one group without, i.e., the condition group includes 23 unipolar and bipolar persons, and the control group includes 32 persons without depression. We use convolutional neural networks to classify the depressed and nondepressed patients. Moreover, different levels of depression were classified. Finally, we trained a model that predicts Montgomery{\r A}sberg Depression Rating Scale scores. We achieved an average F1-score of 0.70 for detecting the control and condition groups. The mean squared error for score prediction was approximately 4.0.\&nbsp;},
	isbn = {9781450369145},
	doi = {10.1145/334744410.1145/3347444.3356238},
	url = {http://dl.acm.org/citation.cfm?doid=3347444http://dl.acm.org/citation.cfm?doid=3347444.3356238http://dl.acm.org/ft_gateway.cfm?id=3356238\&ftid=2091091\&dwn=1},
	author = {Joakim Ihle {Frogner} and Farzan Majeed {Noori} and Halvorsen, P{\r a}l and Hicks, Steven and Garcia-Ceja, Enrique and Torresen, Jim and Riegler, Michael},
	editor = {Boll, Susanne and Lee, Jeannie and Meyer, Jochen and Nag, Nitish and Noel E. {O{\textquoteright}Connor}}
}
@inproceedings {dang2019overview,
	title = {Overview of ImageCLEFlifelog 2019: solve my life puzzle and lifelog moment retrieval},
	journal = {CLEF2019 Working Notes},
	volume = {2380},
	year = {2019},
	pages = {09{\textendash}12},
	publisher = {CEUR Workshop Proceedings (CEUR-WS.org)},
	author = {Dang-Nguyen, Duc-Tien and Piras, Luca and Riegler, Michael and Tran, Minh-Triet and Zhou, Liting and Lux, Mathias and Le, Tu-Khiem and Ninh, Van-Tu and Gurrin, Cathal}
}
@inproceedings {26589,
	title = {Performance of Data Enhancements and Training Optimization for Neural Network {\textendash} A Polyp Detection Case Study},
	journal = {IEEE CBMS International Symposium on Computer-Based Medical Systems (CBMS)},
	year = {2019},
	publisher = { IEEE},
	abstract = {Deep learning using neural networks is becoming more and more popular. It is frequently used in areas like video analysis, image retrieval, traffic forecast and speech recognition. In this respect, the learning and training process usually requires a lot of data. However, in many areas, data is scarce which is definitely the case in our medical application scenario, i.e., polyp detection in the gastrointestinal tract. Here, colorectal cancer is on the list of most common cancer types, and often, the cancer arises from benign, adenomatous polyps containing dysplastic cells. Detection and removal of polyps can therefore prevent the development of cancer. Due to high cost, time consumption, patient discomfort and in-accuracy of existing procedures, researchers have started to explore systems for automatic polyp detection to assist and automate current examination procedures. Following the current gained traction for neural networks, and the typical lack of medical data, we explore how data enhancements affect the training and evaluation of the networks in terms of polyp detection accuracy and particularly if it can be used to increase the detection rate. We also experiment with how various training techniques can be used to increase performance. Our experimental results show how data enhancement and training optimization can be used to increase different aspects of the performance, but we also point out mechanisms that have no and even a negative effect.},
	author = {Fredrik Lund {Henriksen} and Jensen, Rune and H{\r a}kon Kvale {Stensland} and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {26630,
	title = {Predicting Peek Readiness-to-Train of Soccer Players Using Long Short-Term Memory Recurrent Neural Networks},
	journal = {2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
	year = {2019},
	publisher = {IEEE},
	abstract = {We are witnessing the emergence of a myriad of hardware and software systems that quantifies sport and physical activities. These are frequently touted as game changers and important for future sport developments. The vast amount of generated data is often visualized in graphs and dashboards, for use by coaches and other sports professionals to make decisions on training and match strategies. Modern machine- learning methods have the potential to further fuel this process by deriving useful insights that are not easily observable in the raw data streams.This paper tackles the problem of deriving peaks in soccer players{\textquoteright} ability to perform from subjective self-reported wellness data collected using the PMSys system. For this, we train a long short-term memory recurrent neural network model using data from two professional Norwegian soccer teams. We show that our model can predict performance peaks in most scenarios with a precision and recall of at least 90\%. Equipped with such insight, coaches and trainers can better plan individual and team training sessions, and perhaps avoid over training and injuries.},
	doi = {10.1109/CBMI.2019.8877406},
	author = {Wiik, Theodor and H{\r a}vard D {Johansen} and Svein Arne {Pettersen} and Baptista, Ivan and Kupka, Tomas and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {26929,
	title = {Predicting Sperm Motility and Morphology using Deep Learning and Handcrafted Features},
	journal = {MediaEval},
	year = {2019},
	publisher = {ceur ws org},
	abstract = {This paper presents the approach proposed by the organizer team (SimulaMet) for MediaEval 2019 Multimedia for Medicine: The Medico Task. The approach uses a data preparation method which is based on global features extracted from multiple frames within each video and then combines this with information about the patient in order to create a compressed representation of each video. The goal is to create a less hardware expensive data representation that still retains the temporal information of the video and related patient data. Overall, the results need some improvement before being a viable option for clinical use.},
	author = {Hicks, Steven and Halvorsen, P{\r a}l and Trine B. {Haugen} and Jorunn M. {Andersen} and Witczak, Oliwia and Pogorelov, Konstantin and Hugo Lewi {Hammer} and Dang-Nguyen, Duc-Tien and Lux, Mathias and Riegler, Michael}
}
@inproceedings {26629,
	title = {Real-time Analysis of Physical Performance Parameters in Elite Soccer},
	journal = {2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
	year = {2019},
	publisher = { IEEE},
	abstract = {Technology is having vast impact on the sports in- dustry, and in particular soccer. All over the world, soccer teams are adapting digital information systems to quantify performance metrics. The goal is to assess strengths and weaknesses of indi- vidual players, training regimes, and play strategies; to improve performance and win games. However, most existing methods rely on post-game analytic. This limits coaches to review games in retrospect without any means to do changes during sessions. In collaboration with an elite soccer club, we have developed Metrix which is a computerized toolkit for coaches to perform real- time monitoring and analysis of the players{\textquoteright} performance. Using sensor technology to track movement, performance parameters are instantly available to coaches through a mobile phone client. Metrix provides coaches with a toolkit to individualize training load to different playing positions on the field, or to the player himself. Our results show that Metrix is able to quantify player performance and propagate it to coaches in real-time during a match or practice, i.e., latency is below 100 ms on the field. In our initial user evaluation, the coaches express that this is a valuable asset in day-to-day work.},
	doi = {10.1109/CBMI.2019.8877422},
	author = {Andreassen, Kim and Johansen, Dag and Johansen, H{\r a}vard and Baptista, Ivan and Svein Arne {Pettersen} and Riegler, Michael and Halvorsen, P{\r a}l}
}
@misc {smeaton2019report,
	title = {Report from the SIGMM Emerging Leaders Symposium 2018},
	howpublished = {ACM SIGMultimedia Records},
	volume = {10},
	year = {2019},
	pages = {2},
	publisher = {ACM},
	author = {Smeaton, Alan and Zhang, Hanwang and Riegler, Michael and Jia, Jia and Nie, Liqiang}
}
@inproceedings {26812,
	title = {ResUNet++: An Advanced Architecture for Medical Image Segmentation},
	journal = {2019 IEEE International Symposium on Multimedia (ISM)},
	year = {2019},
	publisher = {IEEE},
	address = {San Diego, California, USA},
	abstract = {Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer. Towards developing a fully automated model for pixel-wise polyp segmentation, we propose ResUNet++, which is an improved ResUNet architecture for colonoscopic image segmentation. Our experimental evaluations show that the suggested architecture produces good segmentation results on publicly available datasets. Furthermore, ResUNet++ significantly outperforms U-Net and ResUNet, two key state-of-the-art deep learning architectures, by achieving high evaluation scores with a dice coefficient of 81.33\%, and a mean Intersection over Union (mIoU) of 79.27\% for the Kvasir-SEG dataset and a dice coefficient of 79.55\%, and a mIoU of 79.62\% with CVC-612 dataset.},
	keywords = {colonoscopy, deep learning, health informatics, Medical image segmentation, Polyp segmentation, Semantic segmentation},
	author = {Jha, Debesh and Pia H. {Smedsrud} and Riegler, Michael and Johansen, Dag and de Lange, Thomas and Halvorsen, P{\r a}l and H{\r a}vard D {Johansen}}
}
@inproceedings {26820,
	title = {Saga: An Open Source Platform for Training Machine Learning Models and Community-driven Sharing of Techniques},
	journal = {International Conference on Content-Based Multimedia Indexing (CBMI 2019)},
	year = {2019},
	pages = {1-4},
	publisher = {IEEE},
	doi = {10.1109/CBMI.2019.8877455},
	author = {Borgli, Hanna and H{\r a}kon Kvale {Stensland} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {26708,
	title = {Semantic Analysis of Soccer News for Automatic Game Event Classification},
	journal = {2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
	year = {2019},
	publisher = {IEEE},
	abstract = {We are today overwhelmed with information, of which an important part is news. Sports news, in particular, has become very popular, where soccer makes up a big part of this coverage. For sports fans, it can be a time consuming and tedious to keep up with the news that they really care about. In this paper, we present different machine learning methods applied to soccer news from a Norwegian newspaper and a TV station{\textquoteright}s news site to summarize the content in a short and digestible manner. We present a system to collect, index, label, analyze, and present the collected news articles based on the content. We perform a thorough comparison between deep learning and traditional machine learning algorithms on text classification. Furthermore, we present a dataset of soccer news which was collected from two different Norwegian news sites and shared online.\&nbsp;},
	author = {Nordskog, Aanund and Halvorsen, P{\r a}l and Hicks, Steven and H{\r a}kon Kvale {Stensland} and Hugo Lewi {Hammer} and Johansen, Dag and Riegler, Michael}
}
@article {ahmad2019social,
	title = {Social media and satellites: Disaster event detection, linking and summarization},
	journal = {Multimedia Tools and Applications},
	volume = {78},
	number = {3},
	year = {2019},
	pages = {2837{\textendash}2875},
	publisher = {Springer},
	address = {Netherlands},
	author = {Ahmad, Kashif and Pogorelov, Konstantin and Riegler, Michael and Conci, Nicola and Halvorsen, P{\r a}l}
}
@inproceedings {26922,
	title = {Stacked dense optical flows and dropout layers to predict sperm motility and morphology},
	journal = {MediaEval 2019, 27-29 October 2019, Sophia Antipolis, France},
	year = {2019},
	month = {10/2019},
	abstract = {In this paper, we analyse two deep learning methods to predict sperm motility and sperm morphology from sperm videos. We use two different inputs: stacked pure frames of videos and dense optical flows of video frames. To solve this regression task of predicting motility and morphology, stacked dense optical flows and extracted original frames from sperm videos were used with the modified state of the art convolution neural networks. For modifications of the selected models, we have introduced an additional multi-layer perceptron to overcome the problem of over-fitting. The method which had an additional multi-layer perceptron with dropout layers, shows the best results when the inputs consist of both dense optical flows and an original frame of videos.},
	author = {Thambawita, Vajira and Halvorsen, P{\r a}l and Hugo Lewi {Hammer} and Riegler, Michael and Trine B. {Haugen}}
}
@inproceedings {26587,
	title = {Summarizing E-Sports Matches and Tournaments: The Example of Counter-Strike: Global Offensive},
	journal = {International Workshop on Immersive Mixed and Virtual Environment Systems (MMVE)},
	year = {2019},
	publisher = {ACM},
	abstract = {That video and computer games have reached the masses is a well known fact. Furthermore, game streaming and watching other people play video games is another phenomenon that has outgrown its small beginning by far, and game streams, be it live or recorded, are today viewed by millions. E-sports is the result of organized leagues and tournaments in which players can compete in controlled environments and viewers can experience the matches, discuss and criticize, just like in physical sports. \&nbsp;However, as traditional sports, e-sports matches may be long and contain less interesting parts, introducing the challenge of producing well directed summaries and highlights.\&nbsp;In this paper, we describe our efforts to approach the game streaming and e-sports phenomena from a multimedia research point of view. We focus on the challenge of summarizing matches from specific relevant game, Counter-Strike: Global Offensive (CS:GO). We survey related work, describe the rules and structure of the game and identify the main challenges for summarizing e-sports matches. With this contribution, we aim to foster multimedia research in the area of e-sports and game streaming.},
	author = {Lux, Mathias and Halvorsen, P{\r a}l and Dang-Nguyen, Duc-Tien and H{\r a}kon Kvale {Stensland} and Kesavulu, Manoj and Potthast, Martin and Riegler, Michael}
}
@inproceedings {hammer2019threat,
	title = {THREAT: A Large Annotated Corpus for Detection of Violent Threats},
	journal = {2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
	year = {2019},
	pages = {1{\textendash}5},
	publisher = {IEEE},
	author = {Hugo Lewi {Hammer} and Riegler, Michael and {\O}vrelid, Lilja and Velldal, Erik},
	editor = {Gurrin, Cathal}
}
@inproceedings {26547,
	title = {Unsupervised Preprocessing to Improve Generalisation for Medical Image Classification},
	journal = {IEEE 13th International Symposium on Medical Information and Communication Technology (ISMICT)},
	year = {2019},
	publisher = { IEEE},
	abstract = {Automated disease detection in videos and images from the gastrointestinal (GI) tract has received much attention in the last years. However, the quality of image data is often reduced due to overlays of text and positional data.\&nbsp;In this paper, we present different methods of preprocessing such images and we describe our approach to GI disease classification for the Kvasir v2 dataset.\&nbsp;We propose multiple approaches to inpaint problematic areas in the images to improve the anomaly classification, and we discuss the effect that such preprocessing does to the input data.In short, our experiments show that the proposed methods improve the Matthews correlation coefficient by approximately 7\% in terms of better classification of GI anomalies.},
	doi = {10.1109/ISMICT.2019.8743979},
	author = {Kirker{\o}d, Mathias and Borgli, Rune and Thambawita, Vajira and Hicks, Steven and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {garcia2019user,
	title = {User-adaptive models for activity and emotion recognition using deep transfer learning and data augmentation},
	journal = {User Modeling and User-Adapted Interaction},
	year = {2019},
	pages = {1{\textendash}29},
	publisher = {Springer Netherlands},
	author = {Garcia-Ceja, Enrique and Riegler, Michael and Anders K {Kvernberg} and Torresen, Jim}
}
@inproceedings {26928,
	title = {Using 2D and 3D Convolutional Neural Networks to Predict Semen Quality},
	journal = {MediaEval},
	year = {2019},
	publisher = {CEUR Workshop Proceedings},
	abstract = {In this paper, we present the approach of team Jmag\&nbsp;to solve this year{\textquoteright}s Medico Multimedia Task as part of the MediaEval 2019 Benchmark. This year, the task focuses on automatically determining quality characteristics of human sperm through the analysis of microscopic videos of human semen and associated patient data. Our approach is based on deep convolutional neural networks (CNNs) of varying sizes and dimensions. Here, we aim to analyze both the spatial and temporal information present in the videos. The results show that the method holds promise for predicting the motility of sperm, but predicting morphology appears to be more difficult.},
	author = {Rosenblad, Jon-Magnus and Hicks, Steven and H{\r a}kon Kvale {Stensland} and Trine B. {Haugen} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {26930,
	title = {Using Deep Learning to Predict Motility and Morphology of Human Sperm},
	journal = {MediaEval 2019},
	year = {2019},
	publisher = {CEUR Workshop Proceedings},
	abstract = {In the Medico Task 2019, the main focus is to predict sperm quality based on videos and other related data. In this paper, we present the approach of team LesCats\&nbsp;which is based on deep convolution neural networks, where we experiment with different data preprocessing methods to predict the morphology and motility of human sperm. The achieved results show that deep learning is a promising method for human sperm analysis. Out best method achieves a mean absolute error of 8.962\&nbsp;for the motility task and a mean absolute error of 5.303\&nbsp;for the morphology task.},
	author = {Hicks, Steven and Trine B. {Haugen} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {bahirat2019using,
	title = {Using Mr. MAPP for Lower Limb Phantom Pain Management},
	journal = {Proceedings of the 27th ACM International Conference on Multimedia},
	year = {2019},
	pages = {1071{\textendash}1075},
	publisher = {ACM},
	author = {Bahirat, Kanchan and Chung, Yu-Yen and Annaswamy, Thiru and Raval, Gargi and Desai, Kevin and Prabhakaran, Balakrishnan and Riegler, Michael}
}
@inproceedings {haugen2019visem,
	title = {VISEM: a multimodal video dataset of human spermatozoa},
	journal = {Proceedings of the 10th ACM Multimedia Systems Conference},
	year = {2019},
	pages = {261{\textendash}266},
	publisher = {ACM},
	author = {Trine B {Haugen} and Hicks, Steven and Jorunn M {Andersen} and Witczak, Oliwia and Hugo Lewi {Hammer} and Borgli, Rune and Halvorsen, P{\r a}l and Riegler, Michael},
	editor = {Zink, Michael}
}
@inproceedings {26927,
	title = {A Web-Based Software for Training and Quality Assessment in the Image Analysis Workflow for Cardiac T1 Mapping MRI},
	journal = {2019 IEEE International Symposium on Multimedia (ISM)},
	year = {2019},
	publisher = {IEEE},
	abstract = {Medical practice makes significant use of imaging scans such as Ultrasound or MRI as a diagnostic tool. They are used in the visual inspection or quantification of medical parameters computed from the images in post-processing. However, the value of such parameters depends much on the user{\textquoteright}s variability, device, and algorithmic differences. In this paper, we focus on quantifying the variability due to the human factor, which can be primarily addressed by the structured training of a human operator. We focus on a specific emerging cardiovascular \gls{mri} methodology, the T1 mapping, that has proven useful to identify a range of pathological alterations of the myocardial tissue structure. Training, especially in emerging techniques, is typically not standardized, varying dramatically across medical centers and research teams. Additionally, training assessment is mostly based on qualitative approaches. Our work aims to provide a software tool combining traditional clinical metrics and convolutional neural networks to aid the training process by gathering contours from multiple trainees, quantifying discrepancy from local gold standard or standardized guidelines, classifying trainees output based on critical parameters that affect contours variability.},
	doi = {10.1109/ISM46123.2019.00047},
	author = {Edvarda Regine {Eriksen} and Hicks, Steven and Riegler, Michael and Halvorsen, P{\r a}l and Carapella, Valentina}
}
@article {gurrin2019映像情報メディア学会英語論文誌,
	title = {映像情報メディア学会英語論文誌},
	journal = {映像情報メディア学会英語論文誌},
	volume = {7},
	number = {2},
	year = {2019},
	pages = {46{\textendash}59},
	publisher = {{一般社団法人 映像情報メディア学会},
	address = {Japan},
	author = {Gurrin, Cathal and Minh-Triet, Tran and Loko{\v c}, Jakub and H{\"u}rst, Wolfgang and Schoeffmann, Klaus and Joho, Hideo and Leibetseder, Andreas and Zhou, Liting and Duane, Aaron and Duc-Tien, Dang-Nguyen and Riegler, Michael}
}
@misc {25795,
	title = {Automatic Detection of Angiectasia: Evaluation of Deep Learning and Handcrafted Approaches},
	howpublished = { IEEE Conference on Biomedical and Health Informatics (BHI) 2018},
	year = {2018},
	abstract = {Angiectasia, formerly called angiodysplasia, is one of the most frequent vascular lesions and often the cause of gastrointestinal bleedings. Medical specialists assessing videos of examinations reach a detection performance of 16\% for the detection of bleeding to 69\% for the detection of angiectasia. In this paper, we present several machine-learning-based approaches for angiectasia detection in wireless video capsule endoscopy images. The most promising results for pixel-wise localization and frame-wise detection are obtained by the proposed deep learning approach using generative adversarial networks (GANs) with a sensitivity of 88\% and specificity of 99.9\% for pixel-wise localization and a sensitivity of 98\% and a specificity of 100\% for frame-wise detection, which fits the requirements for automatic angiectasia detection in real clinical settings.},
	author = {Pogorelov, Konstantin and Ostroukhova, Olga and Petlund, Andreas and Halvorsen, P{\r a}l and Espeland, H{\r a}vard and Kupka, Tomas and de Lange, Thomas and Griwodz, Carsten and Riegler, Michael}
}
@inproceedings {runeMedico2018,
	title = {Automatic Hyperparameter Optimization in Keras for the MediaEval 2018 Medico Multimedia Task},
	journal = {Working Notes Proceedings of the MediaEval 2018 Workshop},
	year = {2018},
	publisher = {CEUR Workshop Proceedings (CEUR-WS.org)},
	abstract = {This paper details the approach to the MediaEval 2018 Medico Multimedia Task made by the Rune team. The decided upon approach uses a work-in-progress hyperparameter optimization system called Saga. Saga is a system for creating the best hyperparameter finding in Keras, a popular machine learning framework, using Bayesian optimization and transfer learning. In addition to optimizing the Keras classifier configuration, we try manipulating the dataset by adding extra images in a class lacking in images and splitting a commonly misclassified class into two classes.},
	keywords = {automatic hyperparameter optimization, Bayesian optimization, CNN, convolutional neural networks, dataset manipulation, gpyopt, hyperparameter optimization, keras, saga, tensorflow, Transfer Learning},
	author = {Borgli, Rune and Halvorsen, P{\r a}l and Riegler, Michael and H{\r a}kon Kvale {Stensland}}
}
@inbook {25869,
	title = {Camera Synchronization for Panoramic Videos},
	booktitle = {MediaSync},
	year = {2018},
	month = {03/2018},
	pages = {565-592},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-319-65840-7_20},
	url = {https://doi.org/10.1007/978-3-319-65840-7_20},
	author = {Vamsidhar Reddy {Gaddam} and Langseth, Ragnar and H{\r a}kon Kvale {Stensland} and Griwodz, Carsten and Riegler, Michael and Kupka, Tomas and Espeland, H{\r a}vard and Johansen, Dag and Johansen, H{\r a}vard and Halvorsen, P{\r a}l},
	editor = {Montagud, Mario and Cesar, Pablo and Boronat, Fernando and Jansen, Jack}
}
@inproceedings {26172,
	title = {Challenges and Opportunities within Personal Life Archives},
	journal = {Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval},
	year = {2018},
	month = {07/2018},
	pages = {335-343},
	publisher = {ACM Press},
	address = {New York, NY, USA},
	abstract = {Nowadays, almost everyone holds some form or other of a personal life archive. Automatically maintaining such an archive is an activity that is becoming increasingly common, however without automatic support the users will quickly be overwhelmed by the volume of data and will miss out on the potential benefits that lifelogs provide. In this paper we give an overview of the current status of lifelog research and propose a concept for exploring these archives. We motivate the need for new methodologies for indexing data, organizing content and supporting information access. Finally we will describe challenges to be addressed and give an overview of initial steps that have to be taken, to address the challenges of organising and searching personal life archives.},
	isbn = {9781450350464},
	doi = {10.1145/3206025.3206040},
	url = {http://dl.acm.org/citation.cfm?doid=3206025},
	author = {Dang-Nguyen, Duc-Tien and Riegler, Michael and Zhou, Liting and Gurrin, Cathal},
	editor = {Aizawa, Kiyoharu and Lew, Michael and Satoh, Shin{\textquoteright}ichi}
}
@inproceedings {25950,
	title = {Comprehensible Reasoning and Automated Reporting of Medical Examinations Based on Deep Learning Analysis},
	journal = {Proceedings of the 9th ACM Multimedia Systems Conference},
	year = {2018},
	pages = {490-493},
	publisher = {ACM},
	address = {Amsterdam, Netherlands},
	isbn = {978-1-4503-5192-8},
	doi = {10.1145/3204949.3208113},
	author = {Hicks, Steven and Pogorelov, Konstantin and Lux, Mathias and Jeppsson, Mattis and Kristin Ranheim {Randel} and de Lange, Thomas and Sigrun Losadal {Eskeland} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {25794,
	title = {Deep Learning and Handcrafted Feature Based Approaches for Automatic Detection of Angiectasia},
	journal = {2018 IEEE Conference on Biomedical and Health Informatics (BHI)},
	year = {2018},
	pages = {365-368},
	publisher = {IEEE},
	abstract = {Angiectasia, formerly called angiodysplasia, is one of the most frequent vascular lesions and often the cause of gastrointestinal bleedings. Medical specialists assessing videos or images of examinations reach a detection performance of 16\% for the detection of bleeding to 69\% for the detection of angiectasia. This shows that automatic detection to support medical experts can be useful. In this paper, we present several machine learning-based approaches for angiectasia detection in wireless video capsule endoscopy frames. In summary, the most promising results for pixel-wise localization and framewise detection are obtained by the proposed deep learning method using generative adversarial networks (GANs). Using this approach, we achieve a sensitivity of 88\% and specificity of 99.9\% for pixel-wise localization, and a sensitivity of 98\% and a specificity of 100\% for frame-wise detection. Thus, the results demonstrate the capability of using deep learning for automatic angiectasia detection in real clinical settings.\&nbsp;},
	keywords = {Angiectasia, computer aided diagnosis, deep learning, Machine learning, video capsular endoscopy},
	doi = {10.1109/BHI.2018.8333444},
	author = {Pogorelov, Konstantin and Ostroukhova, Olga and Petlund, Andreas and Halvorsen, P{\r a}l and de Lange, Thomas and Espeland, H{\r a}vard and Kupka, Tomas and Griwodz, Carsten and Riegler, Michael}
}
@inproceedings {25954,
	title = {Deep Learning and Hand-crafted Feature Based Approaches for Polyp Detection in Medical Videos},
	journal = {31st IEEE CBMS International Symposium on Computer-Based Medical Systems},
	year = {2018},
	pages = {381-386},
	publisher = {IEEE},
	address = {Karlstad, Sweden},
	issn = {2372-9198},
	doi = {10.1109/CBMS.2018.00073},
	author = {Pogorelov, Konstantin and Ostroukhova, Olga and Jeppsson, Mattis and Espeland, H{\r a}vard and Griwodz, Carsten and de Lange, Thomas and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {26259,
	title = {Deep learning approaches for flood classification and flood aftermath detection},
	journal = {Working Notes Proceedings of the MediaEval 2018 Workshop},
	volume = {2283},
	year = {2018},
	publisher = {CEUR-WS.org},
	address = {Sophia Antipolis, France},
	abstract = {This paper presents the method proposed by team UTAOS for MediaEval 2018 Multimedia Satellite Task: Emergency Response for Flooding Events. In the first challenge, we mainly rely on object and scene level features extracted through multiple deep models pre-trained on the ImageNet and Places datasets. The object and scene-level features are combined using early, late and double fusion techniques achieving an average F1-score of 65.03\%, 60.59\% and 63.58\%, respectively. For the second challenge, we rely on a convolutional neural networks (CNNs) and a transfer learning-based classification approach achieving an average F1-score of 62.30\% and 61.02\% for run 1 and run 2, respectively.},
	author = {Said, Naina and Pogorelov, Konstantin and Ahmad, Kashif and Riegler, Michael and Ahmad, Nasir and Ostroukhova, Olga and Halvorsen, P{\r a}l and Conci, Nicola}
}
@inproceedings {26207,
	title = {Deep Learning Based Disease Detection Using Domain Specific Transfer Learning},
	journal = {MediaEval 2018},
	year = {2018},
	publisher = {MediaEval},
	abstract = {In this paper, we present our approach for the Medico Multimedia\&nbsp;Task as part of the MediaEval 2018 Benchmark. Our method is based on convolutional neural networks (CNNs), where we compare how fine-tuning, in the context of transfer learning, from different source domains (general versus medical domain) affect classification\&nbsp;performance. The preliminary results show that fine-tuning models trained on large and diverse datasets is favorable, even when the model{\textquoteright}s source domain has little to no resemblance to the new target.\&nbsp;},
	keywords = {convolutional neural networks, deep learning, Gastrointestinal Disease Detection},
	author = {Hicks, Steven and Pia H. {Smedsrud} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {26171,
	title = {Depresjon: A Motor Activity Database of Depression Episodes in Unipolar and Bipolar Patients},
	journal = {Proceedings of the 9th ACM Multimedia Systems Conference},
	year = {2018},
	month = {06/2018},
	pages = {472-477},
	publisher = {ACM Press},
	address = {New York, NY, USA},
	abstract = {Wearable sensors measuring different parts of people{\textquoteright}s activity are a common technology nowadays. In research, data collected using these devices also draws attention. Nevertheless, datasets containing sensor data in the field of medicine are rare. Often, data is non-public and only results are published. This makes it hard for other researchers to reproduce and compare results or even collaborate. In this paper we present a unique dataset containing sensor data collected from patients suffering from depression. The dataset contains motor activity recordings of 23 unipolar and bipolar depressed patients and 32 healthy controls. For each patient we provide sensor data over several days of continuous measuring and also some demographic data. The severity of the patients{\textquoteright} depressive state was labeled using ratings done by medical experts on the Montgomery-Asberg Depression Rating Scale (MADRS). In this respect, the here presented dataset can be useful to explore and understand the association between depression and motor activity better. By making this dataset available, we invite and enable interested researchers the possibility to tackle this challenging and important societal problem.},
	isbn = {9781450351928},
	doi = {10.1145/3204949.3208125},
	author = {Garcia-Ceja, Enrique and Riegler, Michael and Jakobsen, Petter and T{\o}rresen, Jim and Nordgreen, Tine and Ketil J. {Oedegaard} and Ole Bernt {Fasmer}},
	editor = {Cesar, Pablo and Zink, Michael and Murray, Niall}
}
@inproceedings {25956,
	title = {Dissecting Deep Neural Networks for Better Medical Image Classification and Classification Understanding},
	journal = {31st IEEE CBMS International Symposium on Computer-Based Medical Systems},
	year = {2018},
	publisher = {IEEE},
	address = {Karlstad, Sweden},
	issn = {2372-9198},
	doi = {10.1109/CBMS.2018.00070},
	author = {Hicks, Steven and Riegler, Michael and Pogorelov, Konstantin and de Lange, Thomas and Kristin Ranheim {Randel} and Kim V. {{\r A}nonsen} and Jeppsson, Mattis and Halvorsen, P{\r a}l and Sigrun Losadal {Eskeland}}
}
@inproceedings {26250,
	title = {Efficient Live and on-Demand Tiled HEVC 360 VR Video Streaming},
	journal = {2018 IEEE International Symposium on Multimedia (ISM)},
	year = {2018},
	month = {12/2018},
	pages = {81-88},
	publisher = {IEEE},
	address = {Taichung, Taiwan},
	abstract = {With 360{\textopenbullet} panorama video technology becoming commonplace, the need for efficient streaming methods for such videos arises. We go beyond the existing on-demand solutions and present a live streaming system which strikes a trade-off between bandwidth usage and the video quality in the user{\textquoteright}s field-of-view. We have created an architecture that combines RTP and DASH to deliver 360{\textopenbullet} VR content to a Huawei set-top-box and a Samsung Galaxy S7. Our system multiplexes a single HEVC hardware decoder to provide faster quality switching than at the traditional GOP boundaries. We demonstrate the performance and illustrate the trade-offs through real-world experiments where we can report comparable bandwidth savings to existing on-demand approaches, but with faster quality switches when the field-of- view changes.\&nbsp;},
	doi = {10.1109/ISM.2018.00022},
	author = {Jeppsson, Mattis and Espeland, H{\r a}vard and Kupka, Tomas and Langseth, Ragnar and Petlund, Andreas and Qiaoqiao, Peng and Xue, Chuansong and Pogorelov, Konstantin and Riegler, Michael and Johansen, Dag and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@inproceedings {26274,
	title = {GameStory Task at MediaEval 2018},
	journal = {Proceeding of the MediaEval Benchmarking Initiative for Multimedia Evaluation},
	year = {2018},
	month = {10/2018},
	publisher = {CEUR Workshop Proceedings},
	abstract = {That video games have reached the masses is well known. Moreover, game streaming and watching other people play video games is a phenomenon that has outgrown its small beginnings. Game video streams, be it live or recorded, are viewed by millions. E-sports is the result of organized leagues and tournaments in which players can compete in controlled environments and viewers can experience the matches, discuss and criticize, just like in physical sports. In the GameStory task, taking place the first time in 2018, we approach the game streaming and e-sports phenomena from a multimedia research side. We focus on the task of summarizing matches using a specific relevant game,\&nbsp;Counter-Strike: Global Offensive, as a case study. With the help of ZNIPE.tv, we provide a data set of high quality data and meta data from competitive tournaments and aim to foster research in the area of e-sports and game streaming.},
	author = {Lux, Mathias and Riegler, Michael and Dang-Nguyen, Duc-Tien and Larson, Marcus and Potthast, Martin and Halvorsen, P{\r a}l}
}
@inproceedings {26039,
	title = {HINDSIGHT: An R-Based Framework Towards Long Short Term Memory (LSTM) Optimization},
	journal = {Multimedia Systems Conference (MMSys)},
	year = {2018},
	publisher = {ACM},
	author = {Kousias, Konstantinos and Riegler, Michael and Alay, {\"O}zg{\"u} and Argyriou, Antonios}
}
@inproceedings {26251,
	title = {The Importance of Medical Multimedia},
	journal = {2018 ACM Multimedia Conference (MM {\textquoteright}18)},
	year = {2018},
	month = {10/2018},
	pages = {2106-2108},
	publisher = {ACM Press},
	address = {New York, NY, USA},
	abstract = {Multimedia research is becoming more and more important for the medical domain, where an increasing number of videos and images are integrated in the daily routine of surgical and diagnostic work. While the collection of medical multimedia data is not an issue, appropriate tools for efficient use of this data are missing. This includes management and inspection of the data, visual analytics, as well as learning relevant semantics and using recognition results for optimizing surgical and diagnostic processes. The characteristics and requirements in this interesting but challenging field are different than the ones in classic multimedia domains. Therefore, this tutorial gives a general introduction to the field, provides a broad overview of specific requirements and challenges, discusses existing work and open challenges, and elaborates in detail how machine learning approaches can help in multimedia-related fields to improve the performance of surgeons/clinicians.\&nbsp;},
	isbn = {9781450356657},
	doi = {10.1145/3240508.3241475},
	url = {http://dl.acm.org/citation.cfm?doid=3240508http://dl.acm.org/citation.cfm?doid=3240508.3241475http://dl.acm.org/ft_gateway.cfm?id=3241475\&ftid=2009687\&dwn=1},
	author = {Riegler, Michael and Halvorsen, P{\r a}l and M{\"u}nzer, Bernd and Schoeffmann, Klaus}
}
@article {26173,
	title = {An interview with Miriam Redi},
	journal = {ACM SIGMultimedia Records},
	volume = {10},
	year = {2018},
	month = {Nov-04-2019},
	pages = {2 - 2},
	publisher = {ACM},
	doi = {10.1145/321024110.1145/3210241.3210243},
	url = {http://dl.acm.org/citation.cfm?doid=3210241http://dl.acm.org/citation.cfm?doid=3210241.3210243http://dl.acm.org/ft_gateway.cfm?id=3210243\&ftid=1960403\&dwn=1},
	author = {Riegler, Michael and Redi, Miriam}
}
@article {26174,
	title = {An interview with Prof. Alan Smeaton},
	journal = {ACM SIGMultimedia Records},
	volume = {9},
	year = {2018},
	month = {Sep-01-2018},
	pages = {1 - 1},
	publisher = {ACM},
	doi = {10.1145/317842210.1145/3178422.3178423},
	url = {http://dl.acm.org/citation.cfm?doid=3178422http://dl.acm.org/citation.cfm?doid=3178422.3178423http://dl.acm.org/ft_gateway.cfm?id=3178423\&ftid=1938302\&dwn=1},
	author = {Smeaton, Alan and Riegler, Michael}
}
@misc {42548,
	title = {Kunstig Intelligens - Muligheter, Utfordringer og en Plan for Norge},
	year = {2018},
	publisher = {Teknologiradet},
	address = {Oslo, Norway},
	isbn = {978-82-92447-94-9},
	url = {https://teknologiradet.no/wp-content/uploads/sites/105/2018/09/Rapport-Kunstig-intelligens-og-maskinlaering-til-nett.pdf},
	author = {Lovett, Hilde and Nassehi, Damoun and Fosse, Erik and Siri B {Hatlen} and Madsen, Steinar and Hans Olav {Melberg} and Riegler, Michael}
}
@inproceedings {26256,
	title = {Medico Multimedia Task at MediaEval 2018},
	journal = {Working Notes Proceedings of the MediaEval 2018 Workshop},
	year = {2018},
	publisher = {CEUR Workshop Proceedings},
	abstract = {The Medico: Multimedia for Medicine Task, running for the second time as part of MediaEval 2018, focuses on detecting abnormalities, diseases, anatomical landmarks and other findings in images captured by medical devices in the gastrointestinal tract. The task is described, including the use case and its challenges, the dataset with ground truth, the required participant runs and the evaluation metrics.},
	author = {Pogorelov, Konstantin and Riegler, Michael and Halvorsen, P{\r a}l and Hicks, Steven and Kristin Ranheim {Randel} and Dang-Nguyen, Duc-Tien and Lux, Mathias and Ostroukhova, Olga and de Lange, Thomas}
}
@inproceedings {26205,
	title = {The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning},
	journal = {MediaEval 2018},
	year = {2018},
	month = {10/2018},
	publisher = {MediaEval},
	address = {Nice, France},
	abstract = {In this paper, we present our approach for the 2018 Medico Task classifying diseases in the gastrointestinal tract. We have proposed a system based on global features and deep neural networks. The best approach combines two neural networks and the reproducible experimental results signify the efficiency of the proposed model with an accuracy rate of 95.80\%, a precision of 95.87\%, and an F1-score of 95.80\%.},
	keywords = {CNN, deep learning, Gastrointestinal Disease Detection, Global Features, Medico-Task 2018, Transfer Learning},
	author = {Thambawita, Vajira and Jha, Debesh and Riegler, Michael and Halvorsen, P{\r a}l and Hugo Lewi {Hammer} and H{\r a}vard D. {Johansen} and Johansen, Dag}
}
@article {26167,
	title = {Mental Health Monitoring with Multimodal Sensing and Machine Learning: A Survey},
	journal = {Pervasive and Mobile Computing},
	volume = {51},
	year = {2018},
	month = {12/2018},
	pages = {1-26},
	publisher = {Elsevier},
	abstract = {Personal and ubiquitous sensing technologies such as smartphones have allowed the continuous collection of data in an unobtrusive manner. Machine learning methods have been applied to continuous sensor data to predict user contextual information such as location, mood, physical activity, etc. Recently, there has been a growing interest in leveraging ubiquitous sensing technologies for mental health care applications, thus, allowing the automatic continuous monitoring of different mental conditions such as depression, anxiety, stress, and so on. This paper surveys recent research works in mental health monitoring systems (MHMS) using sensor data and machine learning. We focused on research works about mental disorders/conditions such as: depression, anxiety, bipolar disorder, stress, etc. We propose a classification taxonomy to guide the review of related works and present the overall phases of MHMS. Moreover, research challenges in the field and future opportunities are also discussed.},
	doi = {https://doi.org/10.1016/j.pmcj.2018.09.003},
	author = {Garcia-Ceja, Enrique and Riegler, Michael and Nordgreen, Tine and Jakobsen, Petter and Ketil J. {Oedegaard} and T{\o}rresen, Jim}
}
@article {26245,
	title = {Methodology to develop machine learning algorithms to improve performance in gastrointestinal endoscopy},
	journal = {World Journal of Gastroenterology},
	volume = {45},
	year = {2018},
	month = {12/2018},
	pages = {5057-5062},
	publisher = {Baishideng Publishing Group Inc},
	abstract = {Assisted diagnosis using artificial intelligence has been a holy grail in medical research for many years, and recent developments in computer hardware have enabled the narrower area of machine learning to equip clinicians with potentially useful tools for computer assisted diagnosis (CAD) systems. However, training and assessing a computer{\textquoteright}s ability to diagnose like a human are complex tasks, and successful outcomes depend on various factors. We have focused our work on gastrointestinal (GI) endoscopy because it is a cornerstone for diagnosis and treatment of diseases of the GI tract. About 2.8 million luminal GI (esophageal, stomach, colorectal) cancers are detected globally every year, and although substantial technical improvements in endoscopes have been made over the last 10-15 years, a major limitation of endoscopic examinations remains operator variation. This translates into a substantial inter-observer variation in the detection and assessment of mucosal lesions, causing among other things an average polyp miss-rate of 20\% in the colon and thus the subsequent development of a number of post-colonoscopy colorectal cancers. CAD systems might eliminate this variation and lead to more accurate diagnoses. In this editorial, we point out some of the current challenges in the development of efficient computer-based digital assistants. We give examples of proposed tools using various techniques, identify current challenges, and give suggestions for the development and assessment of future CAD systems.\&nbsp;},
	doi = {10.3748/wjg.v24.i45.5057},
	url = {https://www.wjgnet.com/1007-9327/abstract/v24/i45/5057.htm},
	author = {de Lange, Thomas and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {25953,
	title = {Mimir: An Automatic Reporting and Reasoning System for Deep Learning based Analysis in the Medical Domain},
	journal = {Proceedings of the 9th ACM Multimedia Systems Conference},
	year = {2018},
	pages = {369-374},
	publisher = {ACM},
	address = {Amsterdam, Netherlands},
	isbn = {978-1-4503-5192-8},
	doi = {10.1145/3204949.3208129},
	author = {Hicks, Steven and Lux, Mathias and de Lange, Thomas and Kristin Ranheim {Randel} and Jeppsson, Mattis and Pogorelov, Konstantin and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {26170,
	title = {Motor Activity Based Classification of Depression in Unipolar and Bipolar Patients},
	journal = {2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)},
	year = {2018},
	month = {06/2018},
	pages = {316-321},
	publisher = {IEEE},
	address = {Karlstad, Sweden},
	abstract = {Wearable sensors measuring different parts of people{\textquoteright}s activity are a common technology nowadays. Data created using these devices holds a lot of potential besides measuring the quantity of daily steps or calories burned, since continuous recordings of heart rate and activity levels usually are collected. Furthermore, there is an increasing awareness in the field of psychiatry on how these activity data relates to various mental health issues such as changes in mood, personality, inability to cope with daily problems or stress and withdrawal from friends and activities. In this paper we present the analysis of a unique dataset containing sensor data collected from patients suffering from depression. The dataset contains motor activity recordings of 23 unipolar and bipolar depressed patients and 32 healthy controls. We apply machine learning to classify patients into depressed and nondepressed. For evaluation of the algorithms, leave one patient out validation is performed. The best results achieved are an F1 score of 0.73 and a MCC of 0.44. The overall findings show that sensor data contains information that can be used to determine the depression status of a person.},
	issn = {2372-9198},
	doi = {10.1109/CBMS.2018.00062},
	url = {https://ieeexplore.ieee.org/document/8417257/http://xplorestaging.ieee.org/ielx7/8410976/8417175/08417257.pdf?arnumber=8417257},
	author = {Garcia-Ceja, Enrique and Riegler, Michael and Jakobsen, Petter and Torresen, Jim and Nordgreen, Tine and Ketil J. {Oedegaard} and Ole Bernt {Fasmer}},
	editor = {Kane, Bridget}
}
@article {25870,
	title = {Multimodal analysis of user behavior and browsed content under different image search intents},
	journal = {International Journal of Multimedia Information Retrieval},
	volume = {7},
	year = {2018},
	month = {Jan-03-2018},
	pages = {29 - 41},
	publisher = { Springer},
	abstract = {The motivation or intent of a search for content may vary between users and use-cases. Knowledge and understanding of these underlying objectives may therefore be important in order to return appropriate search results, and studies of user search intent are emerging in information retrieval to understand\&nbsp;why\&nbsp;a user is searching for a particular type of content. In the context of image search, our work targets automatic recognition of users{\textquoteright} intent in an early stage of a search session. We have designed seven different search scenarios under the intent conditions of\&nbsp;finding\&nbsp;items,\&nbsp;re-finding\&nbsp;items and\&nbsp;entertainment. Moreover, we have collected facial expressions, physiological responses, eye gaze and implicit user interactions from 51 participants who performed seven different search tasks on a custom-built image retrieval platform, and we have analyzed the users{\textquoteright} spontaneous and explicit reactions under different intent conditions. Finally, we trained different machine learning models to predict users{\textquoteright} search intent from the visual content of the visited images, the user interactions and the spontaneous responses. Our experimental results show that after fusing the visual and user interaction features, our system achieved the F-1 score of 0.722 for classifying three classes in a user-independent cross-validation. Eye gaze and implicit user interactions, including mouse movements and keystrokes are the most informative features for intent recognition. In summary, the most promising results are obtained by modalities that can be captured unobtrusively and online, and the results therefore demonstrate the potential of including intent-based methods in multimedia retrieval platforms.},
	issn = {2192-6611},
	doi = {10.1007/s13735-018-0150-6},
	url = {http://link.springer.com/10.1007/s13735-018-0150-6http://link.springer.com/content/pdf/10.1007/s13735-018-0150-6.pdfhttp://link.springer.com/article/10.1007/s13735-018-0150-6/fulltext.htmlhttp://link.springer.com/content/pdf/10.1007/s13735-018-0150-6.pdf},
	author = {Soleymani, Mohammad and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {25951,
	title = {OpenSea - Open Search Based Classification Tool},
	journal = {Proceedings of the 9th ACM Multimedia Systems Conference},
	year = {2018},
	pages = {363-368},
	publisher = {ACM},
	address = {Amsterdam, Netherlands},
	isbn = {978-1-4503-5192-8},
	doi = {10.1145/3204949.3208128},
	author = {Pogorelov, Konstantin and Albisser, Zeno and Ostroukhova, Olga and Lux, Mathias and Johansen, Dag and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {26168,
	title = {Overview of ImageCLEF 2018: Challenges, datasets and evaluation},
	journal = {ImageCLEF 2018},
	year = {2018},
	month = {08/2018},
	publisher = { Springer},
	abstract = {This paper presents an overview of the ImageCLEF 2018 evaluation campaign, an event that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) Labs 2018. ImageCLEF is an ongoing initiative (it started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval with the aim of providing information access to collections of images in various usage scenarios and domains. In 2018, the 16th edition of ImageCLEF ran three main tasks and a pilot task: (1) a\&nbsp;caption prediction\&nbsp;task that aims at predicting the caption of a figure from the biomedical literature based only on the figure image; (2) a\&nbsp;tuberculosis\&nbsp;task that aims at detecting the tuberculosis type, severity and drug resistance from CT (Computed Tomography) volumes of the lung; (3) a\&nbsp;LifeLog\&nbsp;task (videos, images and other sources) about daily activities understanding and moment retrieval, and (4) a pilot task on\&nbsp;visual question answering\&nbsp;where systems are tasked with answering medical questions. The strong participation, with over 100 research groups registering and 31 submitting results for the tasks, shows an increasing interest in this benchmarking campaign.},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-98932-7_28},
	author = {Ionescu, Bogdan and M{\"u}ller, Henning and Villegas, Mauricio and Alba Garc{\'\i}a Se {de Herrera} and Eickhoff, Carsten and Andrearczyk, Vincent and Yashin Dicente {Cid} and Liauchuk, Vitali and Kovalev, Vassili and Sadid A {Hasan} and Ling, Yan and Farri, Oladimeji and Liu, Joey and Lungren, Matthew and Dang-Nguyen, Duc-Tien and Prias, Luca and Riegler, Michael and Zhou, Liting and Lux, Mathias and Gurrin, Cathal},
	editor = {Bellot, Patrice}
}
@inproceedings {dang2018overview,
	title = {Overview of ImageCLEFlifelog 2018: daily living understanding and lifelog moment retrieval},
	journal = {Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum},
	year = {2018},
	month = {09/2018},
	publisher = {CEUR Workshop Proceedings},
	address = {Avignon, France},
	url = {http://ceur-ws.org/Vol-2125/},
	author = {Dang-Nguyen, Duc-Tien and Piras, Luca and Riegler, Michael and Zhou, Liting and Lux, Mathias and Gurrin, Cathal},
	editor = {Cappellato, Linda}
}
@article {thomee2018sharing,
	title = {Sharing and reproducibility in ACM SIGMM},
	journal = {ACM SIGMultimedia Records},
	volume = {10},
	number = {2},
	year = {2018},
	pages = {1},
	publisher = {ACM},
	doi = {10.1145/3264706.3264707},
	url = {https://dl.acm.org/citation.cfm?id=3264707},
	author = {Thomee, Bart and Riegler, Michael and de Simone, Francesca and Simon, Gwendal}
}
@article {26257,
	title = {Social Media and Satellites. Disaster event detection, linking and summarization},
	journal = {Multimedia Tools and Applications},
	volume = {78},
	year = {2018},
	pages = {2837{\textendash}2875},
	publisher = {Springer},
	address = {US},
	abstract = {Being able to automatically link social media and satellite imagery holds large opportunities for research, with a potentially considerable impact on society. The possibility of integrating different information sources opens in fact to new scenarios where the wide coverage of satellite imaging can be used as a collector of the fine-grained details provided by the social media. Remote-sensed data and social media data can well complement each other, integrating the wide perspective provided by the satellite view with the information collected locally, being it textual, audio, or visual. Among the possible applications, natural disasters are certainly one of the most interesting scenarios, where global and local perspectives are needed at the same time.In this paper, we present a system called JORD that is able to autonomously collect social media data (including the text analysis in local languages) about technological and environmental disasters, and link it automatically to remote-sensed data. Moreover, in order to ensure the quality of retrieved information, JORD is equipped with a hierarchical filtering mechanism relying on the temporal information and the content analysis of retrieved multimedia data.To show the capabilities of the system, we present a large number of disaster events detected by the system, and we evaluate both the quality of the provided information about the events and the usefulness of JORD from potential users viewpoint, using crowdsourcing.},
	keywords = {Event Detection, Information retrieval, Natural Disaster, Social Media},
	doi = {10.1007/s11042-018-5982-9},
	author = {Ahmad, Kashif and Pogorelov, Konstantin and Riegler, Michael and Conci, Nicola and Halvorsen, P{\r a}l}
}
@inproceedings {26275,
	title = {Team ORG @ GameStory Task 2018},
	journal = {Proceeding of the MediaEval Benchmarking Initiative for Multimedia Evaluation},
	year = {2018},
	month = {10/2018},
	publisher = {MediaEval},
	abstract = {This paper describes the approach of the organizers{\textquoteright} team for a submission to the GameStory task at MediaEval 2018. Goal of the task is to provide a summary of a \&nbsp;match of Counter Strike: Global Offensive\&nbsp;(CS:GO), a popular e-sports game, that boils down a long game to it{\textquoteright}s most important events and delivers a story on the progress of the match. Our approach was to provide match statistics and overlay them with events and highlights of the game. We focused on ends of critical rounds, i.e. the rounds where one team took the lead over the other one, and kill streaks, where one player eliminated a substantial number of other players in short time.\&nbsp;},
	author = {Lux, Mathias and Riegler, Michael and Dang-Nguyen, Duc-Tien and Larson, Marcus and Potthast, Martin and Halvorsen, P{\r a}l}
}
@article {25860,
	title = {Top-Down Saliency Detection Driven by Visual Classification},
	journal = {Computer Vision and Image Understanding},
	volume = {172},
	year = {2018},
	pages = {67-76},
	publisher = {Elsevier},
	abstract = {This paper presents an approach for saliency detection able to emulate the integration of the top-down (task-controlled) and bottom-up (sensory information) processes involved in human visual attention. In particular, we first learn how to generate saliency when a specific visual task has to be accomplished. Afterwards, we investigate if and to what extentthe learned saliency maps can support visual classification in nontrivial cases. To achieve this, we propose SalClass-Net, a CNN framework consisting of two networks jointly trained: a) the first one computing top-down saliency mapsfrom input images, and b) the second one exploiting the computed saliency maps for visual classification.To test our approach, we collected a dataset of eye-gaze maps, using a Tobii T60 eye tracker, by asking several subjectsto look at images from the Stanford Dogs dataset, with the objective of distinguishing dog breeds.Performance analysis on our dataset and other saliency benchmarking datasets, such as POET, showed that Sal-ClassNet outperforms state-of-the-art saliency detectors, such as SalNet and SALICON. Finally, we also analyzedthe performance of SalClassNet in a fine-grained recognition task and found out that it yields enhanced classificationaccuracy compared to Inception and VGG-19 classifiers. The achieved results, thus, demonstrate that 1) condition-ing saliency detectors with object classes reaches state-of-the-art performance, and 2) explicitly providing top-downsaliency maps to visual classifiers enhances accuracy.},
	doi = {10.1016/j.cviu.2018.03.005},
	author = {Murabito, Francesca and Spampinato, Concetto and Palazzo, Simone and Giordano, Daniela and Pogorelov, Konstantin and Riegler, Michael}
}
@inproceedings {26249,
	title = {Tradeoffs using Binary and Multiclass Neural Network Classification for Medical Multidisease Detection},
	journal = {2018 IEEE International Symposium on Multimedia (ISM)},
	year = {2018},
	month = {12/2018},
	pages = {1-8},
	publisher = {IEEE},
	abstract = {The interest in neural networks has increased sig- nificantly, and the application of this type of machine learning is vast, ranging from natural image classification to medical image segmentation. However, many users of neural networks tend to use them as a black box tool. They do not access all of the possible variations, nor take into account the respective classification accuracies and costs. In our work, we focus on multiclass image classification, and in this research, we shed light on the trade-offs between systems using a single multiclass classification and multiple binary classifiers, respectively. We have tested the these classifiers on several modern neural network architectures, including DenseNet, Inception v3, Inception ResNet v2, Xception, NASNet and MobileNet. We have compared several aspects of the performance of these architectures during training and testing using both classification styles. We have compared classification speed and several classification accuracy metrics. Here, we present the results from experiments on a total of 99 networks: 11 multiclass and 88 individual binary networks, for an 8-class classification of medical images. In short, using multiple binary classification networks resulted in a 7\% increase in the average F1 score, a 1\% increase in average accuracy, a 1\% increase in precision, and a 4\% increase in average recall. However, on average, such a multi-network style performed the classification 7.6 times slower compared to a single network multiclass implementation. These collective findings show that both approaches can be applied to modern neural network structures. Several binary networks will often give increased classification accuracy, but at the cost of classification speed and resource consumption.\&nbsp;},
	doi = {10.1109/ISM.2018.00009},
	author = {Tor Jan Derek {Berstad} and Riegler, Michael and Espeland, H{\r a}vard and de Lange, Thomas and Pia H. {Smedsrud} and Pogorelov, Konstantin and H{\r a}kon Kvale {Stensland} and Halvorsen, P{\r a}l}
}
@inproceedings {26258,
	title = {Transfer learning with prioritized classification and training dataset equalization for medical objects detection},
	journal = {Working Notes Proceedings of the MediaEval 2018 Workshop},
	year = {2018},
	publisher = {CEUR Workshop Proceedings},
	abstract = {This paper presents the method proposed by the organizer team (SIMULA) for MediaEval 2018 Multimedia for Medicine: the Medico Task. We utilized the recent transfer-learning-based image classification methodology and focused on how easy it is to implement multi-class image classifiers in general and how to improve the classification performance without deep neural network model redesign. The goal for this was both to provide a baseline for the Medico task and to show the\&nbsp; performance of out-of-the-box classifiers for the medical use-case scenario.},
	author = {Ostroukhova, Olga and Pogorelov, Konstantin and Riegler, Michael and Dang-Nguyen, Duc-Tien and Halvorsen, P{\r a}l}
}
@inproceedings {26254,
	title = {Using preprocessing as a tool in medical image detection},
	journal = {MediaEval 2018},
	year = {2018},
	month = {10/2018},
	publisher = {MediaEval},
	address = {Nice, France},
	abstract = {In this paper, we describe our approach to gastrointestinal disease classification for the medico task at MediaEval 2018. We propose multiple ways to inpaint problematic areas in the test and training set to help with classification. We discuss the effect that preprocessing does to the input data with respect to removing regions with sparse information. We also discuss how preprocessing affects the training and evaluation of a dataset that is limited in size. We will also compare the different inpainting methods with transfer learning using a convolutional neural network.},
	keywords = {classification, Image processing, Machine Leanring},
	author = {Kirker{\o}d, Mathias and Thambawita, Vajira and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {25467,
	title = {Building a disclosed lifelog dataset: challenges, principles and processes},
	journal = {Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing (CBMI 2017)},
	year = {2017},
	month = {06/2017},
	publisher = {ACM/IEEE},
	doi = {10.1145/3095713.3095736},
	url = {http://doras.dcu.ie/21823/1/Dang-Nguyen_et_al._2017.pdf},
	author = {Nguyen, Dang and Tien, Duc and Zhou, Liting and Gupta, Rashmi and Riegler, Michael and Gurrin, Cathal},
	editor = {Bertini, Marco}
}
@inproceedings {25268,
	title = {ClusterTag: Interactive Visualization, Clustering and Tagging Tool for Big Image Collections},
	journal = {ACM International Conference on Multimedia Retrieval},
	year = {2017},
	month = {06/2017},
	publisher = {ACM},
	address = {Bucharest},
	author = {Pogorelov, Konstantin and Riegler, Michael and Halvorsen, P{\r a}l and Griwodz, Carsten}
}
@inproceedings {25629,
	title = {CNN and GAN Based Satellite and Social Media Data Fusion for Disaster Detection},
	journal = {MediaEval Benchmark 2017},
	year = {2017},
	month = {09/2017},
	publisher = {CEUR-WS.org},
	address = {Dublin, Ireland},
	author = {Ahmad, Kashif and Pogorelov, Konstantin and Riegler, Michael and Conci, Nicola and Halvorsen, P{\r a}l},
	editor = {Ahmad, Kashif}
}
@inproceedings {25628,
	title = {A comparison of deep learning with global features for gastrointestinal disease detection},
	journal = {MediaEval Benchmark 2017},
	year = {2017},
	month = {09/2017},
	publisher = {CEUR-WS.org},
	address = {Dublin, Ireland},
	author = {Pogorelov, Konstantin and Riegler, Michael and Halvorsen, P{\r a}l and Griwodz, Carsten and de Lange, Thomas and Kristin Ranheim {Randel} and Sigrun Losada {Eskeland} and Dang-Nguyen, Duc-Tien and Olga, Ostroukhova and Lux, Mathias and Spampinato, Concetto},
	editor = {Riegler, Michael}
}
@inproceedings {Suman:2017:DCB:3095713.3095731,
	title = {Detection and Classification of Bleeding Region in WCE Images Using Color Feature},
	journal = {Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing},
	year = {2017},
	pages = {17:1{\textendash}17:6},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {Wireless capsule endoscopy (WCE) is a modern and efficient technology to diagnose complete gastrointestinal tract (GIT) for various abnormalities. Due to long recording time of WCE, it acquires a huge amount of images, which is very tedious for clinical expertise to inspect each and every frame of a complete video footage. In this paper, an automated color feature based technique of bleeding detection is proposed. In case of bleeding, color is a very important feature for an efficient information extraction. Our algorithm is based on statistical color feature analysis and we use support vector machine (SVM) to classify WCE video frames into bleeding and non-bleeding classes with a high processing speed. An experimental evaluation shows that our method has promising bleeding detection performance with sensitivity and specificity higher than existing approaches.},
	keywords = {Bleeding detection, Color feature, Support vector machine, Wireless Capsule Endoscopy},
	isbn = {978-1-4503-5333-5},
	doi = {10.1145/3095713.3095731},
	url = {http://doi.acm.org/10.1145/3095713.3095731},
	author = {Suman, Shipra and Fawnizu Azmadi B. {Hussin} and Aamir Saeed {Malik} and Pogorelov, Konstantin and Riegler, Michael and Shaiw Hooi {Ho} and Hilmi, Ida and Khean Lee {Goh}}
}
@article {25462,
	title = {Efficient disease detection in gastrointestinal videos {\textendash} global features versus neural networks},
	journal = {Multimedia Tools and Applications},
	volume = {76},
	year = {2017},
	month = {11/2017},
	pages = {22493{\textendash}22525},
	publisher = {ACM/Springer},
	abstract = {Analysis of medical videos from the human gastrointestinal (GI) tract for detection and localization of abnormalities like lesions and diseases requires both high precision and recall. Additionally, it is important to support efficient, real-time processing for live feedback during (i) standard colonoscopies and (ii) scalability for massive population-based screening, which we conjecture can be done using a wireless video capsule endoscope (camera-pill). Existing related work in this field does neither provide the necessary combination of accuracy and performance for detecting multiple classes of abnormalities simultaneously nor for particular disease localization tasks. In this paper, a complete end-to-end multimedia system is presented where the aim is to tackle automatic analysis of GI tract videos. The system includes an entire pipeline ranging from data collection, processing and analysis, to visualization. The system combines deep learning neural networks, information retrieval, and analysis of global and local image features in order to implement multi-class classification, detection and localization. Furthermore, it is built in a modular way, so that it can be easily extended to deal with other types of abnormalities. Simultaneously, the system is developed for efficient processing in order to provide real-time feedback to the doctors and for scalability reasons when potentially applied for massive population-based algorithmic screenings in the future. Initial experiments show that our system has multiclass detection accuracy and polyp localization precision at least as good as state-of-the-art systems, and provides additional novelty in terms of real-time performance, low resource consumption and ability to extend with support for new classes of diseases.},
	keywords = {Algorithmic screening, Automatic disease detection, Deep learning neural networks, Global and local image features, Information retrieval, Medical, performance evaluation},
	issn = {1380-7501},
	doi = {10.1007/s11042-017-4989-y},
	url = {https://link.springer.com/article/10.1007\%2Fs11042-017-4989-y},
	author = {Pogorelov, Konstantin and Riegler, Michael and Sigrun Losada {Eskeland} and de Lange, Thomas and Johansen, Dag and Griwodz, Carsten and Peter Thelin {Schmidt} and Halvorsen, P{\r a}l}
}
@phdthesis {25800,
	title = {EIR - A Medical Multimedia System for Efficient Computer Aided Diagnosis},
	volume = {PhD},
	year = {2017},
	month = {02/2017},
	school = {University of Oslo},
	address = {University of Oslo},
	author = {Riegler, Michael}
}
@inproceedings {25338,
	title = {EIR: changing the scene of automatic detection software for gastrointestinal endoscopy},
	journal = {World Congress of GI Endoscopy},
	year = {2017},
	month = {02/2017},
	publisher = {World Endoscopic Organisation},
	address = {Hyderabad, India},
	author = {Sigrun L. {Eskeland} and Aabakken, Lars and Riegler, Michael and Pogorelov, Konstantin and Peter Thelin {Schmidt} and Johansen, Dag and Halvorsen, P{\r a}l},
	editor = {de Lange, Thomas}
}
@inproceedings {25335,
	title = {Finding equilibrium for gym ownership distribution based on game dynamics in Pok{\'e}mon GO game},
	journal = {ACM International Workshop on Massively Multiuser Virtual Environments},
	year = {2017},
	month = {07/2017},
	pages = {1-6},
	publisher = {ACM},
	address = {Taiwan},
	isbn = {978-1-4503-5006-8},
	doi = {10.1145/3083207.3083208},
	author = {Ahmed, Iffat and Riegler, Michael and Petlund, Andreas and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@article {25265,
	title = {From Annotation to Computer-Aided Diagnosis: Detailed Evaluation of a Medical Multimedia System},
	journal = {ACM Transactions on Multimedia Computing, Communications, and Applications},
	volume = {13},
	year = {2017},
	publisher = {ACM},
	author = {Riegler, Michael and Pogorelov, Konstantin and Sigrun L. {Eskeland} and Peter Thelin {Schmidt} and Albisser, Zeno and Johansen, Dag and Griwodz, Carsten and Halvorsen, P{\r a}l and de Lange, Thomas}
}
@inproceedings {25433,
	title = {A Holistic Multimedia System for Gastrointestinal Tract Disease Detection},
	journal = {8th annual ACM conference on Multimedia Systems (MMSys)},
	year = {2017},
	month = {06/2017},
	pages = {112-123},
	publisher = {ACM},
	abstract = {Analysis of medical videos for detection of abnormalities and diseases requires both high precision and recall, but also real-time processing for live feedback and scalability for massive screening of entire populations. Existing work on this field does not provide the necessary combination of retrieval accuracy and performance.\&nbsp;In this paper, a multimedia system is presented where the aim is to tackle automatic analysis of videos from the human gastrointestinal (GI) tract. The system includes the whole pipeline from data collection, processing and analysis, to visualization. The system combines filters using machine learning, image recognition and extraction of global and local image features. Furthermore, it is built in a modular way so that it can easily be extended. At the same time, it is developed for efficient processing in order to provide real-time feedback to the doctors. Our experimental evaluation proves that our system has detection and localisation accuracy at least as good as existing systems for polyp detection, it is capable of detecting a wider range of diseases, it can analyze video in real-time, and it has a low resource consumption for scalability.},
	isbn = {978-1-4503-5002-0},
	doi = {10.1145/3083187.3083189},
	url = {http://dl.acm.org/citation.cfm?id=3083189},
	author = {Pogorelov, Konstantin and Sigrun Losadal {Eskeland} and de Lange, Thomas and Griwodz, Carsten and Kristin Ranheim {Randel} and H{\r a}kon Kvale {Stensland} and Dang-Nguyen, Duc-Tien and Spampinato, Concetto and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l},
	editor = {Cesar, Pablo and Hsu, Cheng-hsin}
}
@misc {25125,
	title = {IAPR Newsletter},
	volume = {39},
	year = {2017},
	month = {02/2017},
	publisher = {IAPR},
	address = {www.iapr.org},
	author = {Hugo Jair {Escalante} and Wan, Jun and Escalera, Sergio and Guyon, Isabelle and Riegler, Michael and Bar{\'o}, Xavier and Müller, Henning and Larson, Martha}
}
@misc {25630,
	title = {Interview column: introduction},
	year = {2017},
	month = {07/2017},
	publisher = {ACM},
	author = {Riegler, Michael and Engelbrecht, Herman and Lux, Mathias}
}
@misc {25631,
	title = {An interview with David Ayman Shamma},
	year = {2017},
	month = {07/2017},
	publisher = {ACM},
	author = {Riegler, Michael}
}
@article {26177,
	title = {An interview with Prof. Ramesh Jain},
	journal = {ACM SIGMultimedia Records},
	volume = {9},
	year = {2017},
	month = {Aug-12-2017},
	pages = {1},
	publisher = {ACM},
	doi = {10.1145/317305810.1145/3173058.3173065},
	url = {http://dl.acm.org/citation.cfm?doid=3173058http://dl.acm.org/citation.cfm?doid=3173058.3173065},
	author = {Jain, Ramesh and Riegler, Michael}
}
@inproceedings {25812,
	title = {JORD: A System for Collecting Information and Monitoring Natural Disasters by Linking Social Media with Satellite Imagery},
	journal = {Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing  - CBMI {\textquoteright}17},
	year = {2017},
	pages = {12:1--12:6},
	publisher = {ACM Press},
	address = {New York, USA},
	abstract = {Gathering information, and continuously monitoring the affected areas after a natural disaster can be crucial to assess the damage, and speed up the recovery process. Satellite imagery is being considered as one of the most productive sources to monitor the after effects of a natural disaster; however, it also comes with a lot of challenges and limitations, due to slow update. It would be beneficiary to link remote sensed data with social media for the damage assessment, and obtaining detailed information about a disaster. The additional information, which are obtainable by social media, can enrich remote-sensed data, and overcome its limitations. To tackle this, we present a system called JORD that is able to autonomously collect social media data about natural disasters, and link it automatically to remote-sensed data. In addition, we demonstrate that queries in local languages that are relevant to the exact position of natural disasters retrieve more accurate information about a disaster event. We also provide content based analysis along with temporal and geo-location based filtering to provide more accurate information to the users. To show the capabilities of the system, we demonstrate that a large number of disaster events can be detected by the system. In addition, we use crowdsourcing to demonstrate the quality of the provided information about the disasters, and usefulness of JORD from potential users point of view.},
	isbn = {9781450353335},
	doi = {10.1145/3095713.3095726},
	url = {http://dl.acm.org/citation.cfm?doid=3095713},
	author = {Ahmad, Kashif and Riegler, Michael and Pogorelov, Konstantin and Halvorsen, P{\r a}l and De Natale, Francesco}
}
@inproceedings {25269,
	title = {The JORD System - Linking sky and social multimedia data to Natural and Technological Disasters},
	journal = {ACM International Conference on Multimedia Retrieval},
	year = {2017},
	month = {06/2017},
	publisher = {ACM},
	address = {Bucharest},
	isbn = {978-1-4503-4701-3},
	doi = {10.1145/3078971.3079013},
	author = {Ahmad, Kashif and Riegler, Michael and Riaz, Ans and Conci, Nicola and Dang-Nguyen, Duc-Tien and Halvorsen, P{\r a}l},
	editor = {Ionescu, Bogdan and Sebe, Nicu}
}
@inproceedings {25332,
	title = {Kvasir: A Multi-Class Image-Dataset for Computer Aided Gastrointestinal Disease Detection},
	journal = {ACM Multimedia Systems},
	year = {2017},
	month = {07/2017},
	publisher = {ACM},
	address = {Taiwan},
	author = {Pogorelov, Konstantin and Ranheim, Kristin and Griwodz, Carsten and de Lange, Thomas and Sigrun L. {Eskeland} and Johansen, Dag and Peter Thelin {Schmidt} and Spampinato, Concetto and Dang-Nguyen, Duc-Tien and Lux, Mathias and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {25267,
	title = {LireSolr - A Visual Information Retrieval Server},
	journal = {ACM International Conference on Multimedia Retrieval},
	year = {2017},
	month = {06/2017},
	publisher = {ACM},
	address = {Bucharest},
	isbn = {978-1-4503-4701-3},
	doi = {10.1145/3078971.3079014},
	author = {Lux, Mathias and Riegler, Michael and Halvorsen, P{\r a}l and MacStravic, Glenn},
	editor = {Ionescu, Bogdan and Sebe, Nicu}
}
@inproceedings {26178,
	title = {Medical Multimedia Information Systems (MMIS)},
	journal = {the 2017 ACMProceedings of the 2017 ACM on Multimedia Conference  - MM {\textquoteright}17},
	year = {2017},
	publisher = {ACM Press},
	address = {Mountain View, California, USANew York, New York, USA},
	isbn = {9781450349062},
	doi = {10.1145/312326610.1145/3123266.3130142},
	url = {http://dl.acm.org/citation.cfm?doid=3123266http://dl.acm.org/citation.cfm?doid=3123266.3130142http://dl.acm.org/ft_gateway.cfm?id=3130142\&ftid=1915055\&dwn=1},
	author = {Schoeffmann, Klaus and M{\"u}nzer, Bernd and Riegler, Michael and Halvorsen, P{\r a}l},
	editor = {Liu, Qiong and Lienhart, Rainer and Wang, Haohong and Sheng-Wei "Kuan-Ta" {Chen} and Boll, Susanne and Chen, Phoebe and Friedland, Gerald and Li, Jia and Yan, Shuicheng}
}
@inproceedings {25632,
	title = {Medical Multimedia Information Systems (MMIS)},
	journal = {ACM Multimedia},
	year = {2017},
	month = {10/2017},
	publisher = {ACM},
	address = {Mountain View},
	author = {Schoeffmann, Klaus and M{\"u}nzer, Bernd and Riegler, Michael and Halvorsen, P{\r a}l},
	editor = {Schoeffmann, Klaus}
}
@inproceedings {25627,
	title = {Multimedia for medicine: the medico Task at mediaEval 2017},
	journal = {MediaEval Benchmark 2017},
	year = {2017},
	month = {09/2017},
	publisher = {CEUR-WS.org},
	address = {Dublin, Ireland},
	author = {Riegler, Michael and Pogorelov, Konstantin and Halvorsen, P{\r a}l and Griwodz, Carsten and de Lange, Thomas and Kristin Ranheim {Randel} and Sigrun Losada {Eskeland} and Dang-Nguyen, Duc-Tien and Lux, Mathias and Spampinato, Concetto},
	editor = {Riegler, Michael}
}
@inproceedings {25334,
	title = {Multimodal Analysis of Image Search Intent - Intent Recognition in Image Search from User Behavior and Visual Content},
	journal = {ACM International Conference on Multimedia Retrieval},
	year = {2017},
	month = {07/2017},
	publisher = {ACM},
	address = {Bucharest},
	isbn = {978-1-4503-4701-3},
	doi = {10.1145/3078971.3078995},
	author = {Soleymani, Mohammad and Riegler, Michael and Halvorsen, P{\r a}l},
	editor = {Ionescu, Bogdan and Sebe, Nicu}
}
@inproceedings {25333,
	title = {Nerthus: A Bowel Preparation Quality Video Dataset},
	journal = {ACM Multimedia Systems},
	year = {2017},
	publisher = {ACM},
	address = {Taipei},
	author = {Pogorelov, Konstantin and Ranheim, Kristin and de Lange, Thomas and Sigrun L. {Eskeland} and Johansen, Dag and Griwodz, Carsten and Spampinato, Concetto and Taschwer, Mario and Lux, Mathias and Peter Thelin {Schmidt} and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {25468,
	title = {Organizer Team at ImageCLEFlifelog 2017: Baseline Approaches for Lifelog Retrieval and Summarization},
	journal = {CLEF 2017},
	year = {2017},
	publisher = {CEUR Workshop Proceedings},
	url = {http://ceur-ws.org/Vol-1866/},
	author = {Zhou, Liting and Piras, Luca and Riegler, Michael and Boato, Giulia and Dang-Nguyen, Duc-Tien and Gurrin, Cathal},
	editor = {Cappellato, Linda}
}
@article {ionescu2017overview,
	title = {Overview of ImageCLEF 2017: Information extraction from images},
	journal = {Experimental IR Meets Multilinguality, Multimodality, and Interaction.},
	volume = {10456},
	year = {2017},
	pages = {315-337},
	publisher = {Springer International Publishing},
	isbn = {978-3-319-65813-1},
	author = {Ionescu, Bogdan and Villegas, Mauricio and Arenas, Helbert and Boato, Giulia and Dang-Nguyen, Duc-Tien and Cid, Dicente and Eickhoff, Carsten and Garcia Seco {de Herrera} and Gurrin, Cathal and Riegler, Michael},
	editor = {M{\"u}ller, Henning}
}
@inproceedings {dang2017overview,
	title = {Overview of ImageCLEFlifelog 2017: Lifelog Retrieval and Summarization},
	journal = {CLEF 2017},
	year = {2017},
	publisher = {CEUR-WS.org},
	author = {Riegler, Michael and Boato, Giulia and Zhou, Liting and Gurrin, Cathal},
	editor = {Dang-Nguyen, Duc-Tien}
}
@inproceedings {25620,
	title = {The Same, Only Different: Contrasting Mobile Operator Behavior from CrowdSourced Dataset},
	journal = {IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2017)},
	year = {2017},
	publisher = {IEEE},
	address = {Montreal, Canada},
	abstract = {Crowdsourcing mobile network performance evaluation is rapidly gaining popularity, with new applications aiming to deliver more accurate and reliable results every day. From the perspective of end-users, these utilities help them estimate the performance of their service provider in terms of throughput, latency and other key performance indicators of the network. In this paper, we build ORCA: Operator Classifier, a Machine Learning (ML) based framework to define and determine the behavior of Mobile Network Operators (MNOs) from crowdsourced datasets. We investigate whether one can differentiate MNOs by using crowdsourced end-to-end network measurements. We consider different performance metrics (e.g. Download (DL)/Upload (UL) data rate, latency, signal strength) and study the impact of them individually but also collectively on differentiating MNOs. We use RTR Open Data, an open dataset of broadband measurements provided by the Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR), to characterize the three major mobile native operators and two virtual operators in Austria. Our results show that ORCA can be used to identify patterns between various mobile systems and disclose their differences from the end-user perspective.},
	author = {Kousias, Konstantinos and Midoglu, Cise and Alay, {\"O}zg{\"u} and Lutu, Andra and Argyriou, Antonios and Riegler, Michael},
	editor = {Kousias, Konstantinos}
}
@inproceedings {24990,
	title = {Smart Lifelogging: Recognizing Human Activities using PHASOR},
	journal = {Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods},
	year = {2017},
	month = {02/2017},
	pages = {761-768},
	publisher = {Spinger},
	address = {Portugal},
	isbn = {978-989-758-222-6},
	doi = {10.5220/0006320907610768},
	author = {Dang-Nguyen, Duc-Tien and Dao, Minh-Son and Riegler, Michael and Gurrin, Cathal},
	editor = {De Marsico, Maria and Gabriella Sanniti {di Baja} and Fred, Ana}
}
@inproceedings {24859,
	title = {ChaLearn Joint Contest on Multimedia Challenges Beyond Visual Analysis: An Overview},
	journal = {International Conference on Pattern Recognition},
	year = {2016},
	month = {12/2016},
	publisher = {IEEE},
	address = {Cancun, Mexico},
	abstract = {This paper provides an overview of the Joint Contest on Multimedia Challenges Beyond Visual Analysis. We organized an academic competition that focused on four problems that require effective processing of multimodal information in order to be solved. Two tracks were devoted to gesture spotting and recognition from RGB-D video, two fundamental problems for human computer interaction. Another track was devoted to a second round of the first impressions challenge of which the goal was to develop methods to recognize personality traits from short video clips. For this second round we adopted a novel collaborative-competitive (i.e., coopetition) setting. The fourth track was dedicated to the problem of video recommendation for improving user experience. The challenge was open for about 45 days, and received outstanding participation: almost 200 participants registered to the contest, and 20 teams sent predictions in the final stage. The main goals of the challenge were fulfilled: the state of the art was advanced considerably in the four tracks, with novel solutions to the proposed problems (mostly relying on deep learning). However, further research is still required. The data of the four tracks will be available to allow researchers to keep making progress in the four tracks.},
	author = {V{\'\i}ctor Ponce {L{\'o}pez} and Wan, Jun and Riegler, Michael and Chen, Baiyu and Clap{\'e}s, Albert and Escalera, Sergio and Guyon, Isabelle and Bar{\'o}, Xavier and Halvorsen, P{\r a}l and M{\"u}ller, Henning}
}
@inproceedings {24472,
	title = {Computer Aided Disease Detection System for Gastrointestinal Examinations},
	journal = {Multimedia Systems Conference 2016},
	year = {2016},
	month = {05/2016},
	publisher = {ACM},
	address = {New York},
	author = {Riegler, Michael and Pogorelov, Konstantin and Markussen, Jonas and Lux, Mathias and H{\r a}kon Kvale {Stensland} and de Lange, Thomas and Griwodz, Carsten and Halvorsen, P{\r a}l and Johansen, Dag and Peter Thelin {Schmidt} and Sigrun L. {Eskeland}}
}
@inproceedings {24480,
	title = {Crowdsourcing as Self Fulfilling Prophecy: Influence of Discarding Workers in Subjective Assessment Tasks},
	journal = {International Workshop on Content-based Multimedia Indexing},
	year = {2016},
	month = {06/2016},
	publisher = {IEEE / ACM},
	author = {Riegler, Michael and Vamsidhar Reddy {Gaddam} and Halvorsen, P{\r a}l and Griwodz, Carsten}
}
@inproceedings {24477,
	title = {Device Lending in PCI Express Networks},
	journal = {Proceedings of the 26th International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV)},
	year = {2016},
	month = {05/2016},
	pages = {10:1-10:6},
	publisher = {ACM},
	abstract = {The challenge of scaling IO performance of multimedia systems to demands of their users has attracted much research. A lot of effort has gone into development of distributed systems that add little latency and computing overhead. For machines in PCI Express (PCIe) clusters, we propose Device Lending as a novel solution which works at a system level. Device Lending achieves low latency and extremely low computing overhead without requiring any application-specific distribution mechanisms. For applications, the remote IO resource appears local. In fact, even the drivers of the operating system remain unaware that hardware resources are located in remote machines. By enabling machines in a PCIe cluster to lend a wide variety of hardware, cluster machines can get temporary access to a pool of IO resources. Network cards, FPGAs, SSDs, and even GPUs can easily be shared among computers. Our proposed solution, Device Lending, works transparently without requiring any modifications to drivers, operating systems or software applications.},
	isbn = {978-1-4503-4356-5},
	doi = {10.1145/2910642.2910650},
	author = {Markussen, Jonas and H{\r a}kon Kvale {Stensland} and Riegler, Michael and Kohmann, Hugo and Nordstr{\o}m, Roy and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@inproceedings {24474,
	title = {Efficient Processing of Videos in a Multi Auditory Environment Using Device Lending of GPUs},
	journal = {The 7th International Conference on Multimedia Systems (MMSys)},
	year = {2016},
	month = {05/2016},
	pages = {36:1-36:4},
	publisher = {ACM},
	isbn = {978-1-4503-4297-1},
	doi = {10.1145/2910017.2910636},
	author = {Pogorelov, Konstantin and Riegler, Michael and Markussen, Jonas and H{\r a}kon Kvale {Stensland} and Halvorsen, P{\r a}l and Griwodz, Carsten and Sigrun L. {Eskeland} and de Lange, Thomas}
}
@inproceedings {24482,
	title = {EIR - Efficient Computer Aided Diagnosis Framework for Gastrointestinal Endoscopies},
	journal = {International Workshop on Content-based Multimedia Indexing},
	year = {2016},
	month = {06/2016},
	publisher = {IEEE / ACM},
	author = {Riegler, Michael and Pogorelov, Konstantin and Halvorsen, P{\r a}l and de Lange, Thomas and Griwodz, Carsten and Johansen, Dag and Peter Thelin {Schmidt} and Sigrun L. {Eskeland}}
}
@inproceedings {24481,
	title = {Explorative Hyperbolic-Tree-Based Clustering Tool for Unsupervised Knowledge Discovery},
	journal = {International Workshop on Content-based Multimedia Indexing},
	year = {2016},
	month = {06/2016},
	publisher = {IEEE / ACM},
	author = {Riegler, Michael and Pogorelov, Konstantin and Lux, Mathias and Halvorsen, P{\r a}l and Griwodz, Carsten and Sigrun L. {Eskeland} and de Lange, Thomas}
}
@inproceedings {24478,
	title = {GPU-accelerated Real-time Gastrointestinal Diseases Detection},
	journal = {CBMS 2016 : The 29th International Symposium on Computer-Based Medical Systems},
	year = {2016},
	month = {07/2016},
	publisher = {IEEE},
	author = {Pogorelov, Konstantin and Riegler, Michael and Halvorsen, P{\r a}l and de Lange, Thomas and Peter Thelin {Schmidt} and Sigrun L. {Eskeland} and Griwodz, Carsten and Johansen, Dag}
}
@inproceedings {24541,
	title = {Heimdallr: a dataset for sport analysis},
	journal = {ACM Multimedia System},
	year = {2016},
	month = {05/2016},
	publisher = {ACM},
	abstract = {In this paper, we present Heimdallr, a dataset that aims to serve two different purposes. The first purpose is action recognition and pose estimation, which requires a dataset of annotated sequences of athlete skeletons. We employed a crowdsourcing platform where people around the world were asked to annotate frames and obtained more than 3000 fully annotated frames for 42 different sequences with a variety of poses and actions. The second purpose is an improved understanding of crowdworkers, and for this purpose, we collected over 10000 written feedbacks from 592 crowdworkers. This is valuable information for crowdsourcing researchers who explore algorithms for worker quality assessment. In addition to the complete dataset, we also provide the code for the application that has been used to collect the data as an open source software.},
	doi = {http://dl.acm.org/citation.cfm?doid=2910017.2910621},
	author = {Riegler, Michael and Dang-Nguyen, Duc-Tien and Winther, B{\r a}rd and Griwodz, Carsten and Pogorelov, Konstantin and Halvorsen, P{\r a}l}
}
@inproceedings {24922,
	title = {ImageCLEF 2017 LifeLog task},
	journal = {Challenges in Machine Learning: Gaming and Education (CiML - NIPS 2016 workshop)},
	year = {2016},
	month = {11/2016},
	publisher = {NIPS},
	address = {Barcelona, Spain},
	author = {Dang-Nguyen, Duc-Tien and Piras, Luca and Riegler, Michael and Gurrin, Cathal and Boato, Giulia and Halvorsen, P{\r a}l}
}
@misc {24951,
	title = {An interview with Judith Redi},
	year = {2016},
	month = {10/2016},
	publisher = {ACM},
	type = {Interview in magazine},
	address = {New York, NY, USA },
	doi = {10.1145/3015445.3015446},
	url = {http://dl.acm.org/citation.cfm?id=3015446\&CFID=703313080\&CFTOKEN=85739970},
	author = {Redi, Judith and Riegler, Michael}
}
@misc {24459,
	title = {An interview with Klara Nahrstedt},
	howpublished = {Interview in SIGMM records},
	volume = {Records Volume 8},
	year = {2016},
	month = {03/2016},
	pages = {8-10},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {An interview for SIGMM records with Klara Nahrsted.},
	keywords = {interview},
	doi = {10.1145/2898367.2898370},
	url = {http://dl.acm.org/citation.cfm?id=2898370\&CFID=762539198\&CFTOKEN=77387626},
	author = {Riegler, Michael}
}
@misc {24460,
	title = {An interview with Wallapak Tavanapong},
	howpublished = {Interview in SIGMM records},
	volume = {Records Volume 8},
	year = {2016},
	month = {03/2016},
	pages = {4-6},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {An interview with Wallapak Tavanapong for SIGMM records.},
	keywords = {interview},
	doi = {10.1145/2898367.2898368},
	url = {http://dl.acm.org/citation.cfm?id=2898368\&CFID=762539198\&CFTOKEN=77387626},
	author = {Riegler, Michael}
}
@inproceedings {24475,
	title = {LIRE - Open Source Visual Information Retrieval},
	journal = {Multimedia System Conference 2016},
	year = {2016},
	month = {05/2016},
	publisher = {ACM},
	address = {New York},
	author = {Lux, Mathias and Riegler, Michael and Pogorelov, Konstantin and Halvorsen, P{\r a}l and Anagnostopoulos, Nektarios}
}
@inproceedings {24857,
	title = {The MediaEval 2016 Context of Experience Task: Recommending Videos Suiting a Watching Situation},
	journal = {MediaEval 2016 Workshop},
	year = {2016},
	month = {10/2016},
	publisher = {CEUR Workshop Proceedings},
	address = {Hilversum, Netherlands},
	abstract = {In this paper we present an overview of the Context of Experience Task: recommending videos suiting a watching situation which is part of the MediaEval 2016 Benchmark. The aim of the task is to explore multimedia content that is watched under a certain situation. The scope of the this years task lies on movies watched during a flight. We hypothesize that users will have different preferences for movies that are watched during a flight compared to when a movie is watched at home or the cinema. This is most probably influenced by the context and the devices used to watch. In the case of being on a flight, the context is clearly different to normal situation (noise, compact, bad air) and also the devices differ (small screens, bad audio quality). The main goal of the task is to estimate if a person would like to watch a certain movie on the airplane or not. As dataset we provide a large collection of movies, collected from an airline, including pre-extracted visual, text and audio features.},
	author = {Riegler, Michael and Spampinato, Concetto and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@inproceedings {24641,
	title = {Multimedia and Medicine: Teammates for Better Disease Detection and Survival},
	journal = {ACM Multimedia},
	year = {2016},
	month = {10/2016},
	publisher = {ACM},
	address = {Amsterdam, The Netherlands, The Netherlands},
	doi = {http://dx.doi.org/10.1145/2964284.2976760},
	author = {Riegler, Michael and Griwodz, Carsten and Spampinato, Concetto and de Lange, Thomas and Sigrun L. {Eskeland} and Pogorelov, Konstantin and Tavanapong, Wallapak and Peter Thelin {Schmidt} and Gurrin, Cathal and Johansen, Dag and Johansen, H{\r a}vard and Halvorsen, P{\r a}l}
}
@inproceedings {24642,
	title = {OpenVQ - A Video Quality Assessment Toolkit},
	journal = {ACM Multimedia},
	year = {2016},
	month = {10/2016},
	publisher = {ACM},
	address = {Amsterdam, The Netherlands, The Netherlands},
	doi = {http://dx.doi.org/10.1145/2964284.2973800},
	author = {Skarseth, Kristian and Bj{\o}rlo, Henrik and Halvorsen, P{\r a}l and Riegler, Michael and Griwodz, Carsten}
}
@inproceedings {24953,
	title = {Proceedings of the MediaEval 2016 Multimedia Benchmark Workshop},
	journal = {Multimedia Benchmark Workshop 2016},
	year = {2016},
	month = {10/2016},
	publisher = {CEUR-WS.org},
	address = {Hilversum, The Netherlands},
	url = {http://ceur-ws.org/Vol-1739/},
	author = {Gravier, Guillaume and Demarty, Claire-H{\'e}l{\`e}ne and Bredin, Herv{\'e} and Ionescu, Bogdan and Boididou, Christina and Dellandrea, Emmanuel and Choi, Jaeyong and Riegler, Michael and Sutcliffe, Richard and Szoke, Igor and Gareth J.F. {Jones} and Larson, Martha}
}
@inproceedings {24473,
	title = {Right inflight? A dataset for exploring the automatic prediction of movies suitable for a watching situation},
	journal = {Multimedia Systems Conference 2016},
	year = {2016},
	month = {05/2016},
	publisher = {ACM},
	address = {New York},
	author = {Riegler, Michael and Spampinato, Concetto and Halvorsen, P{\r a}l and Lux, Mathias and Markussen, Jonas and Pogorelov, Konstantin and Griwodz, Carsten and H{\r a}kon Kvale {Stensland}}
}
@inproceedings {24858,
	title = {Simula @ MediaEval 2016 Context of Experience Task},
	journal = {MediaEval 2016 Workshop},
	year = {2016},
	month = {10/2016},
	publisher = {CEUR Workshop Proceedings},
	address = {Hilversum, Netherlands},
	abstract = {This paper presents our approach for the Context of Multimedia Experience Task of the MediaEval 2016 Benchmark. We present different analyses of the given data using different subsets of data sources and combinations of it. Our approach gives a baseline evaluation indicating that metadata approaches work well but that also visual features can provide useful information for the given problem to solve.},
	author = {Pogorelov, Konstantin and Riegler, Michael and Halvorsen, P{\r a}l and Griwodz, Carsten}
}
@article {24540,
	title = {Tiling in Interactive Panoramic Video: Approaches and Evaluation},
	journal = {IEEE Transactions on Multimedia},
	volume = {18},
	year = {2016},
	pages = {1819-1831},
	publisher = {IEEE},
	abstract = {Interactive panoramic systems are currently on therise. However, one of the major challenges in such a system isthe overhead involved in transferring a full-quality panoramato the client when only a part of the panorama is used toextract a virtual view. Thus, such a system should maximize theuser experience while simultaneously minimizing the bandwidthrequired. In this paper, we apply tiling to deliver different qualitylevels for different parts of the panorama. Tiling has traditionallybeen applied to the delivery of very high-resolution content toclients. Here, we apply similar ideas in a real-time interactivepanoramic video system. A major challenge lies in the movementof such a virtual view, for which clients\&rsquo; regions of interestchange dynamically and independently from each other. Weshow that our algorithms, which progressively increase in qualitytowards the point of the view, manage to (i) reduce the bandwidthrequirement and (ii) provide a similar QoE compared to a fullpanorama system.},
	author = {Vamsidhar Reddy {Gaddam} and Riegler, Michael and Eg, Ragnhild and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@inproceedings {24855,
	title = {Verifying Multimedia Use at MediaEval 2016},
	journal = {MediaEval 2016 Workshop},
	year = {2016},
	month = {10/2016},
	publisher = {CEUR Workshop Proceedings},
	address = {Hilversum, Netherlands},
	abstract = {This paper provides an overview of the Verifying Multimedia Use task that takes places as part of the 2016 MediaEval Benchmark. The task motivates the development of automated techniques for detecting manipulated and misleading use of web multimedia content. Splicing, tampering and reposting videos and images are examples of manipulation that are part of the task definition. For the 2016 edition of the task, a corpus of images/videos and their associated posts is made available, together with labels indicating the appearance of misuse (fake) or not (real) in each case as well as some useful post metadata.},
	author = {Boididou, Christina and Papadopoulos, Symeon and Dang-Nguyen, Duc-Tien and Boato, Giulia and Riegler, Michael and Petlund, Andreas and Kompatsiaris, Yiannis}
}
@inproceedings {24856,
	title = {The VMU Participation @ Verifying Multimedia Use 2016},
	journal = {MediaEval 2016 Workshop},
	year = {2016},
	month = {10/2016},
	publisher = {CEUR Workshop Proceedings},
	address = { Hilversum, Netherlands},
	abstract = {The participating approach predicts whether a tweet, which is accompanied by multimedia content (image/video), is trustworthy (real) or deceptive (fake). We combine two different methods a) one using a semi-supervised learning scheme that leverages the decisions of two independent classifiers to produce a decision and b) one using textual patterns to extract claims about whether a post is fake or real and attribution statements about the content source. The experiments, carried out on the Verifying Multimedia Use dataset, used different combinations of content quality and trust-oriented features, namely tweet-based, user-based and forensics.},
	author = {Boididou, Christina and Papadopoulos, Symeon and Stuart E. {Middleton} and Duc Tien Dang {Nguyen} and Riegler, Michael and Petlund, Andreas and Kompatsiaris, Yiannis}
}
@inproceedings {23431,
	title = {Crowdpinion: Motivating people to share their momentary opinion},
	journal = {Second International Workshop on Gamification for Information Retrieval (GamifIR{\textquoteright}15). Vienna, Austria, March 29 - April 2},
	year = {2015},
	month = {03/2015},
	publisher = {CEUR Workshop Proceedings},
	address = {Vienna, Austria},
	abstract = {Many interesting social studies can be done by asking people about\&nbsp;what they think, feel or experience the moment certain events occur\&nbsp;in their daily lives. These studies can be conducted based on the\&nbsp;event-contingent protocol of the Experience Sampling Method. We\&nbsp;have implemented this protocol in Crowdpinion {\textendash} our software tool\&nbsp;consisting of a web panel where the researchers can set up and\&nbsp;control their studies and a mobile app for the responders. In order\&nbsp;to extend the users{\textquoteright} motivation beyond the will to contribute to\&nbsp;research, we have applied some gamification elements based on\&nbsp;fostering curiosity by gradually revealing the big picture from the\&nbsp;overall study. In this paper we describe the concepts of\&nbsp;Crowdpinion as a research tool, our approach to gamification, how\&nbsp;we tested it in the beta version and present plans for future\&nbsp;improvements.},
	author = {Machnik, Marek and Riegler, Michael and Sen, Sagar}
}
@inproceedings {albisser2015annotation,
	title = {Expert Driven Semi-Supervised Elucidation Tool for Medical Endoscopic Videos},
	journal = {Proceedings of the 6th ACM Multimedia Systems Conference},
	year = {2015},
	month = {03/2015},
	publisher = {ACM},
	author = {Albisser, Zeno and Riegler, Michael and Halvorsen, P{\r a}l and Zhou, Jiang and Griwodz, Carsten and Balasingham, Ilangko and Gurrin, Cathal}
}
@inproceedings {23358,
	title = {Exploitation of Producer Intent in Relation to Bandwidth and QoE for Online Video Streaming Services},
	journal = {NOSSDAV at Multimedia Systems Conference },
	year = {2015},
	month = {03/2015},
	publisher = {ACM},
	address = {Portland, USA},
	abstract = {This paper is the product of recent advances in research on users{\textquoteright} intent during multimedia content retrieval. Our goal is to save bandwidth while streaming video clips from a browsable on-demand service, while maintaining or even improving the users{\textquoteright} quality of experience (QoE). Understanding user intent allows us to predict whether streaming a particular video in a low quality constitutes a reduced QoE for a user. However, many VoD streaming services today are used by users for a wide variety of reasons, meaning that user intent cannot be inferred from their use of the service alone. However, our investigation demonstrates that user intent does in most cases coincide with producer intent. We can also demonstrate that the latter can be inferred from the content itself as well as associated metadata. By transitivity, we can choose a default video quality that satisfies the users QoE in the majority of cases.\&nbsp;},
	author = {Riegler, Michael and Calvet, Lilian and Calvet, Amandine and Halvorsen, P{\r a}l and Griwodz, Carsten}
}
@inproceedings {23569,
	title = {Introduction to a Task on Context of Experience: Recommending Videos Suiting a Watching Situation},
	journal = {MediaEval Benchmarking Initiative for Multimedia Evaluation},
	year = {2015},
	month = {09/2015},
	publisher = {CEUR Workshop Proceedings},
	address = {Wurzen, Germany},
	author = {Riegler, Michael and Spampinato, Concetto and Markussen, Jonas and Halvorsen, P{\r a}l and Griwodz, Carsten}
}
@inproceedings {23567,
	title = {Media Synchronization and Sub-Event Detection in Multi-User Image Collections},
	journal = {ACM HuEvent},
	year = {2015},
	month = {10/2015},
	publisher = {ACM},
	address = {Brisbane, Australia},
	abstract = {Personal media capturing devices, such as smartphones or personal image and video cameras, are rarely synchronized. As a result, common tasks, like event detection and summarization across different multi-user media galleries, are considerably impeded and error-prone. In this paper, we investigate different approaches for the synchronization of image collections using visual information only. We perform a thorough evaluation of the performance of several global features on three datasets. Additionally, we explore the feasibility of common clustering algorithms for the detection of sub-events in the presence of synchronization misalignment.},
	keywords = {Media synchronization, MediaEval, multi-user image collection, sub-event detection},
	author = {Zaharieva, Maia and Riegler, Michael}
}
@inproceedings {23432,
	title = {Playing Around the Eye Tracker - A Serious Game Based Dataset},
	journal = {Second International Workshop on Gamification for Information Retrieval (GamifIR{\textquoteright}15) },
	year = {2015},
	month = {03/2015},
	publisher = {CEUR Workshop Proceedings},
	address = {Vienna, Austria},
	abstract = {In this paper we present a dataset of visual saliency segments\&nbsp;associated to social images, collected through a mobile game\&nbsp;in conjunction with a crowdsourcing campaign. Our data\&nbsp;are collected in the game itself, where players guess what\&nbsp;is depicted in a gradually uncovered image. The game mechanics\&nbsp;allow us to collect information about which image\&nbsp;segments are required by the user to correctly recover the\&nbsp;image content. The provided dataset can be applied to both,\&nbsp;computer vision and image retrieval algorithms, and it aims\&nbsp;to contribute to the understanding of human visual perception\&nbsp;and attention. Moreover, the end objective is to test the\&nbsp;game as a potential substitute to professional eye tracking\&nbsp;systems.},
	author = {Riegler, Michael and Eg, Ragnhild and Calvet, Lilian and Halvorsen, P{\r a}l and Griwodz, Carsten}
}
@inproceedings {24952,
	title = {Proceedings of the MediaEval 2015 Multimedia Benchmark Workshop},
	journal = {Multimedia Benchmark Workshop, 2015},
	year = {2015},
	month = {09/2015},
	publisher = {CEUR-WS.org},
	address = {Wurzen, Germany},
	url = {ceur-ws.org/Vol-1436},
	author = {Larson, Martha and Ionescu, Bogdan and Sj{\"o}berg, Mats and Anguera, Xavier and Poignant, Johann and Riegler, Michael and Eskevich, Maria and Hauff, Claudia and Sutcliffe, Richard and Gareth J.F. {Jones} and Yang, Yi-Hsuan and Soleymani, Mohammad and Papadopoulos, Symeon}
}
@inproceedings {23430,
	title = {Scientific Hangman: Gamifying Scientific Evidence for General Public},
	journal = {Second International Workshop on Gamification for Information Retrieval (GamifIR{\textquoteright}15). Vienna, Austria, March 29 - April 2},
	year = {2015},
	month = {03/2015},
	pages = {26-33},
	publisher = {CEUR Workshop Proceedings},
	address = {Vienna, Austria},
	abstract = {Governmental and private funding for research in many fields has\&nbsp;resulted in a significant body of scientific evidence. Scientific\&nbsp;evidence or content is made available in the form of thousands of\&nbsp;articles communicated via digital libraries. This evidence is\&nbsp;principally used by researchers, students and on occasions for\&nbsp;societal impact such as commercial exploitation and popular\&nbsp;science communication. How can we gamify communicating a\&nbsp;large amount of scientific evidence to the general public? This is\&nbsp;the question that intrigues us. We present the game of Scientific\&nbsp;Hangman, based on the traditional game of hangman, to\&nbsp;communicate scientific research in a fun manner. The puzzles in\&nbsp;our game are based on automatic summarization of scientific\&nbsp;article abstracts. Players play the game in an attempt to guess a\&nbsp;word given a clue such as a paper abstract. Our first prototype,\&nbsp;was evaluated on a focus group at the Cancer Registry of Norway\&nbsp;by communicating information from invitation letters in cervical\&nbsp;cancer screening. We also evaluated a second prototype of the\&nbsp;game to have feedback on design improvements resulted from the\&nbsp;first prototype.},
	author = {Moazzam, Waqas and Riegler, Michael and Sen, Sagar}
}
@inproceedings {23568,
	title = {Verifying Multimedia Use at MediaEval 2015},
	journal = {MediaEval Benchmarking Initiative for Multimedia Evaluation},
	year = {2015},
	month = {09/2015},
	publisher = {CEUR Workshop Proceedings},
	address = {Wurzen, Germany},
	abstract = {This paper provides an overview of the Verifying Multime-dia Use task that takes places as part of the 2015 MediaEvalBenchmark. The task deals with the automatic detection ofmanipulation and misuse of Web multimedia content. Itsaim is to lay the basis for a future generation of tools thatcould assist media professionals in the process of veri ca-tion. Examples of manipulation include the malicious tam-pering of images and videos, e.g., splicing, removal/additionof elements, while other kinds of misuse include the repost-ing of previously captured multimedia content in a di erentcontext (e.g., a new event) claiming that it was capturedthere. For the 2015 edition of the task, we have generatedand make available a large corpus of past real-world casesof images that were distributed through tweets, along withmanually assigned labels regarding their use, i.e. malicious(fake) versus appropriate (real).},
	author = {Boididou, Christina and Andreadou, Katerina and Papadopoulos, Symeon and Dang-Nguyen, Duc-Tien and Boato, Giulia and Riegler, Michael and Kompatsiaris, Yiannis}
}
@inproceedings {23566,
	title = {Why Design Matters - Crowdsourcing of Complex Tasks},
	journal = {ACM CrowdMM},
	year = {2015},
	month = {10/2015},
	publisher = {ACM},
	address = {Brisbane, Australia},
	abstract = {In this paper, we show how the power of the crowd can be usedto do complex tasks using human pose estimation as a use case.Crowdsourcing tasks are usually not very complex and easy tosolve by workers that are not experienced in a certain topic. Formore complex tasks, it is general recommended to use experiencedworkers or experts. However, we show that tasks can also be morecomplex for non-expert workers and that they produce data that isclose to what experts would report. Therefore, a detailed descriptionof the crowdsourcing campaign, the methods applied and theresults will be given, and we discuss how the obtained data can beused.},
	keywords = {Annotation, Crowdsourcing, Multimedia},
	doi = {http://dx.doi.org/10.1145/2810188.2810190},
	author = {Winther, B{\r a}rd and Riegler, Michael and Griwodz, Carsten and Halvorsen, P{\r a}l},
	editor = {Calvet, Lilian}
}
@inproceedings {2660120,
	title = {A Crowdsourced Data Set of Edited Images Online},
	journal = {CrowdMM {\textquoteright}14: Proceedings of the 2014 International ACM Workshop on Crowdsourcing for Multimedia},
	year = {2014},
	pages = {49{\textendash}52},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-3128-9},
	doi = {http://doi.acm.org/10.1145/2660114.2660120},
	author = {Conotter, Valentina and Dang-Nguyen, Duc-Tien and Riegler, Michael and Boato, Giulia}
}
@inproceedings {2660509,
	title = {Event Understanding in Endoscopic Surgery Videos},
	journal = {HuEvent {\textquoteright}14: Proceedings of the 1st ACM International Workshop on Human Centered Event Understanding from Multimedia},
	year = {2014},
	pages = {17{\textendash}22},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-3120-3},
	doi = {http://doi.acm.org/10.1145/2660505.2660509},
	author = {Guggenberger, Mario and Riegler, Michael and Lux, Mathias and Halvorsen, P{\r a}l}
}
@inproceedings {2563675,
	title = {Fashion 10000: An Enriched Social Image Dataset for Fashion and Clothing},
	journal = {MMSys {\textquoteright}14: Proceedings of the 5th ACM Multimedia Systems Conference},
	year = {2014},
	pages = {41{\textendash}46},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-2705-3},
	doi = {http://doi.acm.org/10.1145/2557642.2563675},
	author = {Loni, Babak and Lei Yen {Cheung} and Riegler, Michael and Bozzon, Alessandro and Gottlieb, Luke}
}
@inproceedings {2660123,
	title = {Getting by with a Little Help from the Crowd: Practical Approaches to Social Image Labeling},
	journal = {CrowdMM {\textquoteright}14: Proceedings of the 2014 International ACM Workshop on Crowdsourcing for Multimedia},
	year = {2014},
	pages = {69{\textendash}74},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-3128-9},
	doi = {http://doi.acm.org/10.1145/2660114.2660123},
	author = {Loni, Babak and Hare, Jonathon and Georgescu, Mihai and Riegler, Michael and Zhu, Xiaofei and Morchid, Mohamed and Dufour, Richard}
}
@inproceedings {2655619,
	title = {Gone: An Interactive Experience for Two People},
	journal = {MM {\textquoteright}14: Proceedings of the ACM International Conference on Multimedia},
	year = {2014},
	pages = {701{\textendash}704},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-3063-3},
	doi = {http://doi.acm.org/10.1145/2647868.2655619},
	author = {Riegler, Michael and Lux, Mathias and Zellot, Christian and Knoch, Lukas and Schnattler, Horst and Napetschnig, Sabrina and Kogler, Julian and Degendorfer, Claus and Spot, Norbert and Zoderer, Manuel}
}
@inproceedings {2654894,
	title = {How {\textquoteright}How{\textquoteright} Reflects What{\textquoteright}s What: Content-based Exploitation of How Users Frame Social Images},
	journal = {MM {\textquoteright}14: Proceedings of the ACM International Conference on Multimedia},
	year = {2014},
	pages = {397{\textendash}406},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-3063-3},
	doi = {http://doi.acm.org/10.1145/2647868.2654894},
	author = {Riegler, Michael and Lux, Mathias and Kofler, Christoph}
}
@inproceedings {19163,
	title = {Mobile Picture Guess: A Crowdsourced Serious Game for Simulating Human Perception},
	journal = {Proceedings of the International Workshop on Social Media for Crowdsourcing and Human Computation},
	year = {2014},
	month = {10/2014},
	publisher = {Springer},
	abstract = {In this paper we present a novel idea that combines a mobilegame with a Crowdsourcing campaign. The game is designed for studiesinto the visual saliency of image segments, where the game objective isfor players to guess what is depicted in an image that is gradually uncovering.Game scores depend on the number of correct answers and thespeed at which these are provided. With these game mechanics, we candetermine the image segments that are most essential to players whenasked to guess the image content, thereby assessing the most salientimage regions. Through the combination of this game scenario and aCrowdsourcing campaign, we also present a way to tackle the rising demandsfor higher salaries in this line of work. By providing workers withan entertaining task, we aim to increase player motivation and hopefullymake them want to play longer than required. In this paper we alsopresent a sample study that evaluates the visual saliency of 200 animalimages from Flickr. We conclude with preliminary results from the studyand with our insights on how this approach can be applied},
	author = {Riegler, Michael and Eg, Ragnhild and Lux, Mathias and Schicho, Markus}
}
@inproceedings {19166,
	title = {Multimodal Synchronization of Image Galleries},
	journal = {MediaEval 2014 Workshop, Barcelona, Catalunya, Spain},
	year = {2014},
	month = {10/2014},
	publisher = {CEUR Workshop Proceedings},
	address = {Barcelona},
	author = {Zaharieva, Maia and Riegler, Michael and Del Fabro, Manfred}
}
@inproceedings {2594789,
	title = {PictureSort: Gamification of Image Ranking},
	journal = {GamifIR {\textquoteright}14: Proceedings of the First International Workshop on Gamification for Information Retrieval},
	year = {2014},
	pages = {57{\textendash}60},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-2892-0},
	doi = {http://doi.acm.org/10.1145/2594776.2594789},
	author = {Lux, Mathias and Guggenberger, Mario and Riegler, Michael}
}
@inproceedings {2582621,
	title = {VideoJot: A Multifunctional Video Annotation Tool},
	journal = {ICMR {\textquoteright}14: Proceedings of International Conference on Multimedia Retrieval},
	year = {2014},
	pages = {534{\textendash}537},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-2782-4},
	doi = {http://doi.acm.org/10.1145/2578726.2582621},
	author = {Riegler, Michael and Lux, Mathias and Charvillat, Vincent and Carlier, Axel and Vliegendhart, Raynor}
}
@inproceedings {19165,
	title = {Work Like a Bee -Taking Advantage of Diligent Crowdsourcing Workers},
	journal = {MediaEval 2014 Workshop, Barcelona, Catalunya, Spain},
	year = {2014},
	month = {10/2014},
	publisher = {CEUR Workshop Proceedings},
	address = {Barcelona, Catalonia/Spain},
	author = {Riegler, Michael and Preben N. {Olsen} and Halvorsen, P{\r a}l}
}
@inproceedings {2483996,
	title = {Annotation of endoscopic videos on mobile devices: a bottom-up approach},
	journal = {MMSys {\textquoteright}13: Proceedings of the 4th ACM Multimedia Systems Conference},
	year = {2013},
	pages = {141{\textendash}145},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-1894-5},
	doi = {http://doi.acm.org/10.1145/2483977.2483996},
	author = {Lux, Mathias and Riegler, Michael}
}
@inproceedings {li2013exploration,
	title = {Exploration of Feature Combination in Geo-visual Ranking for Visual Content-based Location Prediction.},
	journal = {MediaEval 2013 Workshop, Barcelona, Spain},
	year = {2013},
	month = {10/2013},
	address = {CEUR-WS.org},
	author = {Li, Xinchao and Riegler, Michael and Hanjalic, Alan}
}
@inproceedings {riegler13,
	title = {Frame the Crowd: Global Visual Features Labeling boosted with Crowdsourcing Information},
	journal = {MediaEval 2013 Workshop, Barcelona, Spain},
	year = {2013},
	month = {10/2014},
	address = {CEUR-WS.org},
	author = {Riegler, Michael and Lux, Mathias and Kofler, Christoph}
}
@article {maier2022metrics,
	title = {Metrics reloaded: Pitfalls and recommendations for image analysis validation},
	journal = {arXiv preprint arXiv:2206.01653},
	publisher = {Arxiv},
	address = {Arxiv},
	author = {Maier-Hein, Lena and Reinke, Annika and Christodoulou, Evangelia and Glocker, Ben and Godau, Patrick and Isensee, Fabian and Kleesiek, Jens and Kozubek, Michal and Reyes, Mauricio and Riegler, Michael and , }
}
@article {maier2022metrics,
	title = {Metrics reloaded: Pitfalls and recommendations for image analysis validation},
	journal = {arXiv preprint arXiv:2206.01653},
	publisher = {Arxiv},
	address = {Arxiv},
	author = {Maier-Hein, Lena and Reinke, Annika and Christodoulou, Evangelia and Glocker, Ben and Godau, Patrick and Isensee, Fabian and Kleesiek, Jens and Kozubek, Michal and Reyes, Mauricio and Riegler, Michael and , }
}
