@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 {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 {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 {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 {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 {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}
}
@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}
}
@misc {28442,
	title = {MMSys{\textquoteright}22 Grand Challenge on AI-based Video Production for Soccer},
	year = {2022},
	publisher = {arXiv},
	url = {https://arxiv.org/abs/2202.01031},
	author = {Midoglu, Cise and Hicks, Steven and Thambawita, Vajira and Kupka, Tomas 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 {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}}
}
@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}
}
@phdthesis {43136,
	title = {Transparency in Medical Artificial Intelligence},
	volume = {PhD},
	year = {2022},
	author = {Hicks, Steven}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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 {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 {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}
}
@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 {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}
}
@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}
}
@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}}
}
@inproceedings {28317,
	title = {Reproducibility Companion Paper: Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features},
	journal = {Proceedings of the 29th ACM International Conference on Multimedia (MM {\textquoteright}21)},
	year = {2021},
	pages = {3622{\textendash}3626},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {9781450386517},
	doi = {10.1145/3474085.3477933},
	url = {https://dl.acm.org/doi/10.1145/3474085.3477933},
	author = {Heng Tao {Shen} and Zhuang, Yueting and John R. {Smith} and Yang, Yang and Cesar, Pablo and Metze, Florian and Prabhakaran, Balakrishnan and Korhonen, Jari and Su, Yicheng and You, Junyong and Hicks, Steven and Midoglu, Cise}
}
@inproceedings {28211,
	title = {Reproducibility Companion Paper: Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework},
	journal = {Proceedings of the 29th ACM International Conference on Multimedia (MM {\textquoteright}21)},
	year = {2021},
	pages = {3630{\textendash}3632},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {In this companion paper, we provide details of the artifacts to support the replication of "Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework", which was presented at MM{\textquoteright}20. The Inter-intra Contrastive (IIC) framework aims to extract more discriminative temporal information by extending intra-negative samples in contrastive self-supervised learning. In this paper, we first summarize our contribution. Then we explain the file structure of the source code and detailed settings. Since our proposal is a framework which contain a lot of different settings, we provide some custom settings to help other researchers to use our methods easily. The source code is available at https://github.com/BestJuly/IIC.},
	isbn = {9781450386517},
	doi = {10.1145/347408510.1145/3474085.3477939},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3474085https://dl.acm.org/doi/10.1145/3474085.3477939https://dl.acm.org/doi/pdf/10.1145/3474085.3477939},
	author = {Heng Tao {Shen} and Zhuang, Yueting and John R. {Smith} and Yang, Yang and Cesar, Pablo and Metze, Florian and Prabhakaran, Balakrishnan and Tao, Li and Wang, Xueting and Yamasaki, Toshihiko and Chen, Jingjing and Hicks, Steven}
}
@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 {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}
}
@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}
}
@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 {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 {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 {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 {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 {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 {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 {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}
}
@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 {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 {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 {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}
}
@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}
}
@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 {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}
}
@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 {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 {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 {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 {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}
}
