@article {43139,
	title = {Approximate Bayesian Inference Based on Expected Evaluation},
	journal = {Bayesian Analysis},
	volume = {1},
	year = {2023},
	month = {Jan-01-2023},
	publisher = {Project euclid},
	abstract = {Approximate Bayesian computing (ABC) and Bayesian Synthetic likelihood (BSL) are two popular families of methods to evaluate the posterior distribution when the likelihood function is not available or tractable. For existing variants of ABC and BSL, the focus is usually first put on the simulation algorithm, and after that the form of the resulting approximate posterior distribution comes as a consequence of the algorithm. In this paper we turn this around and firstly define a reasonable approximate posterior distribution by studying the distributional properties of the expected discrepancy, or more generally an expected evaluation, with respect to generated samples from the model. The resulting approximate posterior distribution will be on a simple and interpretable form compared to ABC and BSL.Secondly a Markov chain Monte Carlo (MCMC) algorithm is developed to simulate from the resulting approximate posterior distribution. The algorithm was evaluated on a synthetic data example and on the Stepping Stone population genetics model, demonstrating that the proposed scheme has real world applicability. The algorithm demonstrates competitive results with the BSL and sequential Monte Carlo ABC algorithms, but is outperformed by the ABC MCMC.},
	doi = {10.1214/23-BA1368},
	url = {https://projecteuclid.org/journals/bayesian-analysis/volume--1/issue--1/Approximate-Bayesian-Inference-Based-on-Expected-Evaluation/10.1214/23-BA1368.full},
	author = {Hugo Lewi {Hammer} and Riegler, Michael and Tjelmeland, H{\r a}kon}
}
@inproceedings {42498,
	title = {Detecting human embryo cleavage stages using YOLO v5 object detection algorithm},
	journal = {Nordic Artificial Intelligence Research and Development},
	year = {2023},
	pages = {81-93},
	publisher = {Springer},
	author = {Sharma, Akriti and Stensen, Mette and Delbarre, Erwan and Siddiqui, Momin and Haugen, Trine and Riegler, Michael and Hugo Lewi {Hammer}}
}
@inproceedings {43320,
	title = {Predicting the degree of meibomian gland dropout with artificial intelligence},
	journal = {ARVO Annual Meeting},
	year = {2023},
	author = {Fineide, Fredrik and Stor{\r a}s, Andrea and Magn{\o}, Morten and Hugo Lewi {Hammer} and Chen, Xiangjun and Khan, Ayyad and Halvorsen, P{\r a}l and Utheim, Tor and Riegler, Michael}
}
@inproceedings {43255,
	title = {Using explainable artificial intelligence (XAI) to explore factors affecting meibomian gland (MG) dropout},
	journal = {ARVO Annual Meeting},
	year = {2023},
	author = {Stor{\r a}s, Andrea and Fineide, Fredrik and Hugo Lewi {Hammer} and Khan, Ayyad and Magn{\o}, Morten and Xiangjung, Chen and Halvorsen, P{\r a}l and Utheim, Tor and Riegler, Michael}
}
@article {28224,
	title = {Artificial intelligence in dry eye disease},
	journal = {The Ocular Surface},
	volume = {23},
	year = {2022},
	month = {Jan-01-2022},
	pages = {74 - 86},
	publisher = {Elsevier},
	abstract = {Dry eye disease (DED) has a prevalence of between 5 and 50\%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term {\textquoteleft}AI{\textquoteright} is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of\&nbsp;interferometry, slit-lamp and\&nbsp;meibography\&nbsp;images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.},
	keywords = {artificial intelligence, Dry eye disease, Machine learning},
	issn = {15420124},
	doi = {10.1016/j.jtos.2021.11.004},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1542012421001324},
	author = {Stor{\r a}s, Andrea and Str{\"u}mke, Inga and Riegler, Michael and Grauslund, Jakob and Hugo Lewi {Hammer} and Yazidi, Anis and Halvorsen, P{\r a}l and Kjell G. {Gundersen} and Tor P. {Utheim} and Catherine J. {Jackson}}
}
@article {42723,
	title = {Automatic Tracking of the ICSI procedure using Deep Learning},
	journal = {Human Reproduction},
	volume = {37},
	year = {2022},
	month = {07/2022},
	publisher = {Oxford University Press},
	abstract = {Study questionCan deep learning be used to detect and track spermatozoa and the different parts of an ICSI procedure?Summary answerDeep learning can be used as a tool to assist and organize the contents of an ICSI procedure.What is known alreadySperm tracking has been a topic of research and practice for many years, especially in the context of computer-aided sperm analysis (CASA). Recent studies have proposed using deep learning algorithms to track spermatozoa for spermatozoon selection in human and animal samples. One critical part of performing ICSI involves the selection of the {\textquotedblleft}best{\textquotedblright} spermatozoon for injection, but other parts of the procedure may also be of importance. However, as far as we know, tracking using deep learning has not been applied to the ICSI procedure, where detecting instruments and the oocyte could also be helpful in post-analysis and training.Study design, size, durationThe study was performed using three anonymized videos of the ICSI procedure. The frames of the videos were manually annotated by data scientists and verified by an embryologist. The annotations were bounding boxes around specific parts of the ICSI procedure, including sperm, pipettes, and the oocyte. We trained a YOLOv5 model on the collected data, where two videos were used for training and one video for validation.Participants/materials, setting, methodsThe videos of the ICSI procedure were captured at 200x magnification with a DeltaPix camera at Fertilitetssenteret in Oslo, Norway. ICSI was performed using a Nikon ECLIPSE TE2000-S microscope connected with Eppendorf TransferMan 4m micromanipulators. The spermatozoa were immobilised in 5 {\textmu}l Polyvinylpyrrolidone (PVP; CooperSurgical). The videos had a resolution of 1920x1080 and were resized to 640x640 before being processed by the YOLOv5 model. The data will be made public in a later study.Main results and the role of chanceMean average precision (mAP) with the threshold of 0.5 (mAP@.5) is the main quantitative parameter measured in the YOLOv5 model. All the experiments were performed using three-fold cross-validation, where we present the average metrics calculated over the three folds. Overall, the method showed an average mAP@.5 of 0.50 across all predicted classes, which means that the method can track the different components with good accuracy. Looking closer at the individual classes, we see that instruments like the holding pipette and ICSI pipette are detected with high accuracy with a mAP@.5 of 0.87 and 0.94, respectively. The oocyte is also easily tracked with a mAP@.5 of 0.92. The first polar body is well detected with a mAP@.5 of 0.65. The model has issues detecting and tracking individual sperm (both outside and within the pipette), where the method achieved a mAP@.5 of 0.46 for tracking sperm outside the pipette and 0.03 for the sperm inside the pipette. The low score of detecting the sperm in the pipette can be explained by the often unclear visibility of the sperm through the pipette and the low number of training samples.Limitations, reasons for cautionThe limited sample size makes the generalizability of the method difficult to determine. A more extensive evaluation is necessary. Moreover, as the currency study focuses on tracking, patient information and clinical outcome were not included in the analysis.Wider implications of the findingsDeep learning has the potential to aid embryologists to perform successful ICSI through tracking and detection of spermatozoa, pipettes, and the oocyte. This could potentially lead to better internal quality control and teaching possibilities, and hopefully better results.Trial registration numbernot applicable},
	issn = {0268-1161},
	doi = {10.1093/humrep/deac107.261},
	url = {https://academic.oup.com/humrep/article/37/Supplement_1/deac107.261/6619904},
	author = {Hicks, Steven and Thambawita, Vajira and Stor{\r a}s, Andrea and Trine B. {Haugen} and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Riegler, Michael and Mette Haug {Stensen}}
}
@inproceedings {42538,
	title = {Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure},
	journal = {NAIS: Symposium of the Norwegian AI Society },
	volume = {1650},
	year = {2022},
	pages = {1-11},
	publisher = {NAIS 2022},
	abstract = {The\&nbsp;in vitro\&nbsp;fertilization procedure called intracytoplasmic sperm injection can be used to help fertilize an egg by injecting a single sperm cell directly into the cytoplasm of the egg. In order to evaluate, refine and improve the method in the fertility clinic, the procedure is usually observed at the clinic. Alternatively, a video of the procedure can be examined and labeled in a time-consuming process. To reduce the time required for the assessment, we propose an unsupervised method that automatically clusters video frames of the intracytoplasmic sperm injection procedure. Deep features are extracted from the video frames and form the basis for a clustering method. The method provides meaningful clusters representing different stages of the intracytoplasmic sperm injection procedure. The clusters can lead to more efficient examinations and possible new insights that can improve clinical practice. Further on, it may also contribute to improved clinical outcomes due to increased understanding about the technical aspects and better results of the procedure. Despite promising results, the proposed method can be further improved by increasing the amount of data and exploring other types of features.},
	keywords = {clustering, Computer Vision and Pattern Recognition (cs.CV), human reproduction, medical videos, Unsupervised learning},
	isbn = {978-3-031-17029-4},
	doi = {10.1007/978-3-031-17030-0_9},
	author = {Stor{\r a}s, Andrea and Riegler, Michael and Trine B. {Haugen} and Thambawita, Vajira and Hicks, Steven and Hugo Lewi {Hammer} and Kakulavarapu, Radhika and Halvorsen, P{\r a}l and Stensen, Mette}
}
@inproceedings {sharma2022automating,
	title = {Automating tracking of cell division for human embryo development in time lapse videos},
	journal = {Human Reproduction},
	volume = {37},
	year = {2022},
	pages = {I305{\textendash}I306},
	publisher = {Oxford University Press},
	author = {Sharma, A and Kakulavarapu, R and Thambawita, Vajira and Siddiqui, M and Delbarre, E and Riegler, Michael and Hugo Lewi {Hammer} and Stensen, M}
}
@article {42998,
	title = {Automating tracking of cell division for human embryo development in time lapse videos},
	journal = {Human Reproduction},
	volume = {37},
	year = {2022},
	month = {Jun-06-2024},
	publisher = {Oxford University Press},
	issn = {0268-1161},
	doi = {10.1093/humrep/deac107.233},
	url = {https://academic.oup.com/humrep/article-pdf/37/Supplement_1/deac107.233/44306957/deac107.233.pdf},
	author = {Sharma, Akriti and Kakulavarapu, R and Thambawita, Vajira and Siddiqui, M and Delbarre, E and Riegler, Michael and Hugo Lewi {Hammer} and Stensen, M}
}
@article {42541,
	title = {Bias og kvantitativ analyse innen velferd},
	journal = {Tidsskrift for velferdsforskning},
	volume = {25},
	year = {2022},
	pages = {1-24},
	publisher = {Universitetsforlaget},
	address = {Tidsskrift for velferdsforskning},
	abstract = {If{\o}lge Norges nasjonale strategi for kunstig intelligens (2020) er offentlig forvaltning og helse blant Norges satsningsomr{\r a}der for bruk av kunstig intelligens. Maskinl{\ae}ring er en undergruppe av kunstig intelligens med potensiale for {\r a} l{\o}se en rekke utfordringer, men som ogs{\r a} gir opphav til utfordringer. En slik utfordring er bias, eller skjevhet. Et eksempel p{\r a} skjevhet er at tilstedev{\ae}rende ulikheter i samfunnet representeres i datagrunnlaget maskinl{\ae}ringsmodeller utvikles p{\r a}. De resulterende modellene st{\r a}r dermed i fare for {\r a} adoptere og videref{\o}re disse ulikhetene. En utfordring er at skjevhet har ulike definisjoner innen ulike fagomr{\r a}der, og kan ha mange ulike opphav. Vi bidrar til {\r a} l{\o}se denne utfordringen ved {\r a} gi en oversikt over ulike typer skjevhet og deres opphav med illustrasjoner fra et velferdsperspektiv, samt avklarer forskjellen til det n{\ae}rliggende konseptet rettferdighet. Vi demonstrerer utfordringer relatert til databaserte modellers oppf{\o}rsel ved {\r a} benytte maskinl{\ae}ring til {\r a} predikere fremtidig ressursbehov i helsevesenet, spesifikt antall legebes{\o}k i kommuner. Vi demonstrerer ulike typer skjevheter, diskuterer mulige l{\o}sninger og bruker metoder fra forklarbar kunstig intelligens for {\r a} analysere opphavet til skjevheter i forklaringsvariablene. Det finnes ingen universell l{\o}sning for {\r a} h{\r a}ndtere alle typer skjevheter, men skjevhet m{\r a} tas h{\o}yde for i alle deler av en kvantitativ analyse.},
	keywords = {forklarbar kunstig intelligens, Maskinl{\ae}ring, skjevhetsbegreper, velferdsforskning, XGBoost},
	doi = {https://doi.org/10.18261/tfv.25.3.3},
	author = {Stor{\r a}s, Andrea and Prabhu, Robindra and Hugo Lewi {Hammer} and Str{\"u}mke, Inga}
}
@article {42438,
	title = {Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources},
	journal = {Sensors},
	volume = {22},
	year = {2022},
	month = {01-04-2022},
	pages = {2802},
	publisher = {MPDI},
	doi = {10.3390/s22072802},
	url = {https://www.mdpi.com/1424-8220/22/7/2802https://www.mdpi.com/1424-8220/22/7/2802/pdf},
	author = {Siri S. {Eide} and Riegler, Michael and Hugo Lewi {Hammer} and John Bj{\o}rnar {Bremnes}}
}
@article {hammer2022efficient,
	title = {Efficient quantile tracking using an oracle},
	journal = {Applied Intelligence},
	volume = {53},
	year = {2022},
	pages = {289{\textendash}300},
	publisher = {Springer},
	author = {Hugo Lewi {Hammer} and Yazidi, Anis and Riegler, Michael and Rue, H{\r a}vard}
}
@article {hammer2022estimating,
	title = {Estimating tukey depth using incremental quantile estimators},
	journal = {Pattern Recognition},
	volume = {122},
	year = {2022},
	pages = {108339},
	publisher = {Elsevier},
	author = {Hugo Lewi {Hammer} and Yazidi, Anis and Rue, H\aavard}
}
@inproceedings {sharma2022explainable,
	title = {Explainable Artificial Intelligence for Human Embryo Cell Cleavage Stages Analysis},
	journal = {Proceedings of the 3rd ACM Workshop on Intelligent Cross-Data Analysis and Retrieval},
	year = {2022},
	pages = {1{\textendash}8},
	publisher = {ACM},
	author = {Sharma, Akriti and Mette H {Stensen} and Delbarre, Erwan and Trine B {Haugen} and Hugo Lewi {Hammer}}
}
@article {42713,
	title = {Prediction of Schizophrenia from Activity Data using Hidden Markov Model Parameters},
	journal = {Neural Computing and Applications},
	year = {2022},
	publisher = { Springer},
	author = {Boeker, Matthias and Riegler, Michael and Hugo Lewi {Hammer} and Jakobsen, Petter and Halvorsen, P{\r a}l}
}
@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}
}
@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}}
}
@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}
}
@article {mofrad2021enhanced,
	title = {Enhanced Equivalence Projective Simulation: A Framework for Modeling Formation of Stimulus Equivalence Classes},
	journal = {Neural Computation},
	volume = {33},
	year = {2021},
	pages = {483{\textendash}527},
	publisher = {MIT Press},
	doi = {https://doi.org/10.1162/neco_a_01346},
	author = {Asieh Abolpour {Mofrad} and Yazidi, Anis and Samaneh Abolpour {Mofrad} and Hugo Lewi {Hammer} and Arntzen, Erik}
}
@inproceedings {27641,
	title = {HTAD: A Home-Tasks Activities Dataset with Wrist-accelerometer and Audio Features},
	journal = {27th International Conference on Multimedia Modeling},
	year = {2021},
	pages = {196-205},
	publisher = { Springer},
	keywords = {Accelerometer, Activity  recognition, Audio, dataset, Sensor fusion},
	author = {Garcia-Ceja, Enrique and Thambawita, Vajira and Hicks, Steven and Jha, Debesh and Jakobsen, Petter and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {28136,
	title = {HYPERAKTIV: An Activity Dataset from Patients with Attention-Deficit/Hyperactivity Disorder (ADHD)},
	journal = {Proceedings of the 12th ACM Multimedia Systems Conference (MMSys {\textquoteright}21)},
	year = {2021},
	pages = {314{\textendash}319},
	publisher = {ACM},
	abstract = {Machine learning research within healthcare frequently lacks the public data needed to be fully reproducible and comparable. Datasets are often restricted due to privacy concerns and legal requirements that come with patient-related data. Consequentially, many algorithms and models get published on the same topic without a standard benchmark to measure against. Therefore, this paper presents HYPERAKTIV, a public dataset containing health, activity, and heart rate data from patients diagnosed with attention deficit hyperactivity disorder, better known as ADHD. The dataset consists of data collected from 51 patients with ADHD and 52 clinical controls. In addition to the activity and heart rate data, we also include a series of patient attributes such as their age, sex, and information about their mental state, as well as output data from a computerized neuropsychological test. Together with the presented dataset, we also provide baseline experiments using traditional machine learning algorithms to predict ADHD based on the included activity data. We hope that this dataset can be used as a starting point for computer scientists who want to contribute to the field of mental health, and as a common benchmark for future work in ADHD analysis.},
	doi = {10.1145/3458305.3478454},
	url = {https://dl.acm.org/doi/10.1145/3458305.3478454},
	author = {Hicks, Steven and Stautland, Andrea and Ole Bernt {Fasmer} and Fr{\o}land, Wenche and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Mjeldheim, Kristin and Ketil J. {Oedegaard} and Osnes, Berge and Vigdis Elin Gi{\ae}v {Syrstad} and Riegler, Michael and Jakobsen, Petter}
}
@inproceedings {27830,
	title = {Identification of spermatozoa by unsupervised learning from video data},
	journal = {37th Virtual Annual Meeting of the European Society of Human Reproduction and Embryology (ESHRE)},
	year = {2021},
	publisher = {Oxford University Press},
	abstract = {Identification of individual sperm is essential to assess a given sperm sample{\textquoteright}s motility behavior. Existing computer-aided systems need training data based on annotations by professionals, which is resource demanding. On the other hand, data analysed by unsupervised machine learning algorithms can improve supervised algorithms that are more stable for clinical applications. Therefore, unsupervised sperm identification can improve computer-aided sperm analysis systems predicting different aspects of sperm samples. Other possible applications are assessing kinematics and counting of spermatozoa.\&nbsp;},
	author = {Thambawita, Vajira and Trine B {Haugen} and Stensen, M.H. and Witczak, Oliwia and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@article {hammer2021joint,
	title = {Joint tracking of multiple quantiles through conditional quantiles},
	journal = {Information Sciences},
	volume = {563},
	year = {2021},
	pages = {40{\textendash}58},
	publisher = {Elsevier},
	author = {Hugo Lewi {Hammer} and Yazidi, Anis and Rue, H\aavard}
}
@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 {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}
}
@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 {28225,
	title = {Temperature Forecasting using Tower Networks},
	journal = {Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval},
	year = {2021},
	month = {08/2021},
	pages = {18-23},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	isbn = {9781450385299},
	doi = {10.1145/346394410.1145/3463944.3469099},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3463944https://dl.acm.org/doi/10.1145/3463944.3469099https://dl.acm.org/doi/pdf/10.1145/3463944.3469099},
	author = {Siri S. {Eide} and Riegler, Michael and Hugo Lewi {Hammer} and John Bj{\o}rnar {Bremnes}},
	editor = {Dao, Minh-Son and Dang-Nguyen, Duc-Tien and Riegler, Michael}
}
@article {28232,
	title = {Towards better explainable deep learning models for embryo selection in ART},
	journal = {Human Reproduction},
	volume = {36},
	year = {2021},
	month = {Jun-08-2021},
	publisher = {Oxford Academic},
	issn = {0268-1161},
	doi = {10.1093/humrep/deab130.259},
	url = {https://academic.oup.com/humrep/article/doi/10.1093/humrep/deab130.259},
	author = {Sharma, A and Haugen, T and Hugo Lewi {Hammer} and Riegler, Michael and Stensen, M}
}
@article {28061,
	title = {Unraveling the Impact of Land Cover Changes on Climate using Machine Learning and Explainable Artificial Intelligence},
	journal = {Big Data and Cognitive Computing},
	volume = {5},
	number = {4},
	year = {2021},
	pages = {55},
	publisher = {Multidisciplinary Digital Publishing Institute},
	abstract = {A general issue in climate science is to handle big data and run complex and computationally heavy simulations. In this paper, we explore the potential of using machine learning (ML) to spare computational time and optimize data usage. The paper analyzes the effects of changes in land cover (LC), such as deforestation or urbanization, on local climate. Along with green house gas emission, LC changes are known to be important causes of climate change. ML methods were trained to learn the relation between LC changes and temperature changes. The results showed that Random Forest (RF) outperformed other ML methods. Explainable Artificial Intelligence (XAI) was further used to interpret the RF method and explain the impact of different LC changes on temperature. The results mainly agree with the climate science literature, demonstrating that ML methods in combination with XAI can be useful in analyzing the climate effects of LC changes. All parts of the analysis pipeline are explained including data pre-processing, feature extraction, ML training, performance evaluation and XAI.},
	author = {Kolevatova, Anastasiia and Riegler, Michael and Cherubini, Francesco and Hu, Xiangping and Hugo Lewi {Hammer}}
}
@article {yazidi2020achieving,
	title = {Achieving Fair Load Balancing by Invoking a Learning Automata-Based Two-Time-Scale Separation Paradigm},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	year = {2020},
	pages = {1-14},
	publisher = {IEEE},
	author = {Yazidi, Anis and Hassan, Ismail and Hugo Lewi {Hammer} and Oommen, John}
}
@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}
}
@inproceedings {pinto2020analysis,
	title = {Analysis of Optical Brain Signals Using Connectivity Graph Networks},
	journal = {International Cross-Domain Conference for Machine Learning and Knowledge Extraction},
	year = {2020},
	pages = {485{\textendash}497},
	publisher = {Springer},
	author = {Marco Antonio {Pinto-Orellana} and Hugo Lewi {Hammer}}
}
@article {yazidi2020balanced,
	title = {Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow},
	journal = {Cognitive Neurodynamics},
	volume = {14},
	number = {5},
	year = {2020},
	pages = {675{\textendash}687},
	publisher = {Springer},
	author = {Yazidi, Anis and Asieh Abolpour {Mofrad} and Goodwin, Morten and Hugo Lewi {Hammer} and Arntzen, Erik}
}
@misc {27332,
	title = {Big data is not always better {\textendash} prediction of live birth using machine learning on time-lapse videos of human embryos},
	year = {2020},
	publisher = {ESHRE},
	address = {ESHRE virtual 36th Annual Meeting},
	author = {Hicks, Steven and Trine B. {Haugen} and Iliceto, Mario and Hugo Lewi {Hammer} and Jorunn M. {Andersen} and Witczak, Oliwia and Riegler, Michael and Stensen, Mette}
}
@article {pinto2020dyadic,
	title = {Dyadic Aggregated Autoregressive Model (DASAR) for Automatic Modulation Classification},
	journal = {IEEE Access},
	volume = {8},
	year = {2020},
	pages = {156096{\textendash}156103},
	publisher = {IEEE},
	author = {Marco Antonio {Pinto-Orellana} and Hugo Lewi {Hammer}}
}
@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 {mofrad2020equivalence,
	title = {Equivalence projective simulation as a framework for modeling formation of stimulus equivalence classes},
	journal = {Neural computation},
	volume = {32},
	number = {5},
	year = {2020},
	pages = {912{\textendash}968},
	publisher = {{MIT Press},
	abstract = {Stimulus equivalence (SE) and projective simulation (PS) study complex behavior, the former in human subjects and the latter in artificial agents. We apply the PS learning framework for modeling the formation of equivalence classes. For this purpose, we first modify the PS model to accommodate imitating the emergence of equivalence relations. Later, we formulate the SE formation through the matching-to-sample (MTS) procedure. The proposed version of PS model, called the equivalence projective simulation (EPS) model, is able to act within a varying action set and derive new relations without receiving feedback from the environment. To the best of our knowledge, it is the first time that the field of equivalence theory in behavior analysis has been linked to an artificial agent in a machine learning context. This model has many advantages over existing neural network models. Briefly, our EPS model is not a black box model, but rather a model with the capability of easy interpretation and flexibility for further modifications. To validate the model, some experimental results performed by prominent behavior analysts are simulated. The results confirm that the EPS model is able to reliably simulate and replicate the same behavior as real experiments in various settings, including formation of equivalence relations in typical participants, nonformation of equivalence relations in language-disabled children, and nodal effect in a linear series with nodal distance five. Moreover, through a hypothetical experiment, we discuss the possibility of applying EPS in further equivalence theory research.},
	author = {Asieh Abolpour {Mofrad} and Yazidi, Anis and Hugo Lewi {Hammer} and Arntzen, Erik}
}
@article {mofrad2020equivalence,
	title = {Equivalence Projective Simulation as a Framework for Modeling Formation of Stimulus Equivalence Classes},
	journal = {Neural Computation},
	volume = {32},
	number = {5},
	year = {2020},
	pages = {912{\textendash}968},
	publisher = {MIT Press},
	address = {Cambridge, MA, USA},
	issn = {0899-7667},
	doi = {10.1162/neco_a_01274},
	url = {https://doi.org/10.1162/neco_a_01274},
	author = {Asieh Abolpour {Mofrad} and Yazidi, Anis and Hugo Lewi {Hammer} and Arntzen, Erik}
}
@article {mofrad2020equivalence,
	title = {Equivalence projective simulation as a framework for modeling formation of stimulus equivalence classes},
	journal = {Neural computation},
	volume = {32},
	number = {5},
	year = {2020},
	pages = {912{\textendash}968},
	publisher = {{MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~{\textellipsis}},
	author = {Asieh Abolpour {Mofrad} and Yazidi, Anis and Hugo Lewi {Hammer} and Arntzen, Erik}
}
@inproceedings {pontes2020evodynamic,
	title = {EvoDynamic: A Framework for the Evolution of Generally Represented Dynamical Systems and Its Application to Criticality},
	journal = {International Conference on the Applications of Evolutionary Computation (Part of EvoStar)},
	year = {2020},
	pages = {133{\textendash}148},
	publisher = {Springer},
	address = {Cham},
	author = {Pontes-Filho, Sidney and Lind, Pedro and Yazidi, Anis and Zhang, Jianhua and Hugo Lewi {Hammer} and Gustavo BM {Mello} and Sandvig, Ioanna and Tufte, Gunnar and Nichele, Stefano}
}
@article {27265,
	title = {An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classification},
	journal = {ACM Transactions on Computing for Healthcare},
	volume = {1},
	number = {3},
	year = {2020},
	pages = {1-29},
	publisher = {ACM},
	abstract = {Precise and efficient automated identification of Gastrointestinal (GI) tract diseases can help doctors treat more patients and improve the rate of disease detection and identification. Currently, automatic analysis of diseases in the GI tract is a hot topic in both computer science and medical-related journals. Nevertheless, the evaluation of such an automatic analysis is often incomplete or simply wrong. Algorithms are often only tested on small and biased datasets, and cross-dataset evaluations are rarely performed. A clear understanding of evaluation metrics and machine learning models with cross datasets is crucial to bring research in the field to a new quality level. Towards this goal, we present comprehensive evaluations of five distinct machine learning models using Global Features and Deep Neural Networks that can classify 16 different key types of GI tract conditions, including pathological findings, anatomical landmarks, polyp removal conditions, and normal findings from images captured by common GI tract examination instruments. Inour evaluation, we introduce performance hexagons using six performance metrics such as recall, precision, specificity, accuracy, F1-score, and Matthews Correlation Coefficient to demonstrate how to determine the real capabilities of models rather than evaluatingthem shallowly. Furthermore, we perform cross-dataset evaluations using different datasets for training and testing. With these cross-dataset evaluations, we demonstrate the challenge of actually building a generalizable model that could be used across different hospitals. Our experiments clearly show that more sophisticated performance metrics and evaluation methods need to be applied to get reliable models rather than depending on evaluations of the splits of the same dataset, i.e., the performance metrics should always be interpreted together rather than relying on a single metric.},
	author = {Thambawita, Vajira and Jha, Debesh and Hugo Lewi {Hammer} and H{\r a}vard D {Johansen} and Johansen, Dag and Halvorsen, P{\r a}l and Riegler, Michael}
}
@article {yazidi2020game,
	title = {Game-Theoretic Learning for Sensor Reliability Evaluation Without Knowledge of the Ground Truth},
	journal = {IEEE Transactions on Cybernetics},
	year = {2020},
	publisher = {IEEE},
	author = {Yazidi, Anis and Hugo Lewi {Hammer} and Samouylov, Konstantin and Herrera-Viedma, Enrique}
}
@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}
}
@article {kongshavn2020mitigating,
	title = {Mitigating DDoS using weight-based geographical clustering},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {32},
	number = {11},
	year = {2020},
	pages = {e5679},
	publisher = {Wiley Online Library},
	author = {Kongshavn, Madeleine and Haugerud, H\aarek and Yazidi, Anis and Torle {Maseng, iv} and Hugo Lewi {Hammer}}
}
@article {pontes2020neuro,
	title = {A neuro-inspired general framework for the evolution of stochastic dynamical systems: Cellular automata, random Boolean networks and echo state networks towards criticality},
	journal = {Cognitive Neurodynamics},
	volume = {14},
	year = {2020},
	pages = {1{\textendash}18},
	publisher = {Springer},
	url = {https://doi.org/10.1007/s11571-020-09600-x},
	author = {Pontes-Filho, Sidney and Lind, Pedro and Yazidi, Anis and Zhang, Jianhua and Hugo Lewi {Hammer} and Gustavo BM {Mello} and Sandvig, Ioanna and Tufte, Gunnar and Nichele, Stefano}
}
@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}
}
@article {yazidi2020solving,
	title = {Solving Sensor Identification Problem Without Knowledge of the Ground Truth Using Replicator Dynamics},
	journal = {IEEE Transactions on Cybernetics},
	year = {2020},
	pages = {1-9},
	publisher = {IEEE},
	author = {Yazidi, Anis and Marco Antonio {Pinto-Orellana} and Hugo Lewi {Hammer} and Mirtaheri, Peyman and Herrera-Viedma, Enrique}
}
@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}
}
@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 {tatara2019associations,
	title = {Associations Between Immigration-Related User Factors and eHealth Activities for Self-Care: Case of First-Generation Immigrants From Pakistan in the Oslo Area, Norway},
	journal = {JMIR public health and surveillance},
	volume = {5},
	number = {3},
	year = {2019},
	pages = {e11998},
	publisher = {{JMIR Publications Inc., Toronto, Canada},
	author = {Tatara, Naoe and Hugo Lewi {Hammer} and Mirkovic, Jelena and Marte Karoline R {Kj{\o}llesdal} and Hege Kristin {Andreassen}}
}
@inproceedings {26923,
	title = {Extracting temporal features into a spatial domain using autoencoders for sperm video analysis},
	journal = {MediaEval 2019},
	year = {2019},
	month = {10/2019},
	publisher = {CEUR Workshop Proceedings},
	abstract = {In this paper, we present a two-step deep learning method that is used to predict sperm motility and morphology based on video recordings of human spermatozoa. First, we use an autoencoder to extract temporal features from a given semen video and plot these into image-space, which we call feature-images. Second, these feature-images are used to perform transfer learning to predict the motility and morphology values of human sperm. The presented method shows it{\textquoteright}s capability to extract temporal information into spatial domain feature-images which can be used with traditional convolutional neural networks. Furthermore, the accuracy of the predicted motility of a given semen sample shows that a deep learning-based model can capture the temporal information of microscopic recordings of human semen.},
	author = {Thambawita, Vajira and Halvorsen, P{\r a}l and Hugo Lewi {Hammer} and Riegler, Michael and Trine B. {Haugen}}
}
@inproceedings {26689,
	title = {GANEx: A complete pipeline of training, inference and benchmarking GAN experiments},
	journal = {2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
	year = {2019},
	pages = {1-4},
	publisher = {IEEE},
	keywords = {GAN, GANEx, Generative Adversarial Network},
	author = {Thambawita, Vajira and Hugo Lewi {Hammer} and Riegler, Michael and Halvorsen, P{\r a}l}
}
@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}
}
@article {hammer2019new,
	title = {A new quantile tracking algorithm using a generalized exponentially weighted average of observations},
	journal = {Applied Intelligence},
	volume = {49},
	number = {4},
	year = {2019},
	pages = {1406{\textendash}1420},
	publisher = {{Springer},
	author = {Hugo Lewi {Hammer} and Yazidi, Anis and Rue, H\aavard}
}
@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}
}
@article {hammer2019smooth,
	title = {Smooth estimates of multiple quantiles in dynamically varying data streams},
	journal = {Pattern Analysis and Applications},
	year = {2019},
	pages = {1{\textendash}12},
	publisher = {{Springer},
	author = {Hugo Lewi {Hammer} and Yazidi, Anis}
}
@article {mofrad2019solving,
	title = {On solving the SPL problem using the concept of probability flux},
	journal = {Applied Intelligence},
	volume = {49},
	number = {7},
	year = {2019},
	pages = {2699{\textendash}2722},
	publisher = {{Springer},
	author = {Asieh Abolpour {Mofrad} and Yazidi, Anis and Hugo Lewi {Hammer}}
}
@inproceedings {26922,
	title = {Stacked dense optical flows and dropout layers to predict sperm motility and morphology},
	journal = {MediaEval 2019, 27-29 October 2019, Sophia Antipolis, France},
	year = {2019},
	month = {10/2019},
	abstract = {In this paper, we analyse two deep learning methods to predict sperm motility and sperm morphology from sperm videos. We use two different inputs: stacked pure frames of videos and dense optical flows of video frames. To solve this regression task of predicting motility and morphology, stacked dense optical flows and extracted original frames from sperm videos were used with the modified state of the art convolution neural networks. For modifications of the selected models, we have introduced an additional multi-layer perceptron to overcome the problem of over-fitting. The method which had an additional multi-layer perceptron with dropout layers, shows the best results when the inputs consist of both dense optical flows and an original frame of videos.},
	author = {Thambawita, Vajira and Halvorsen, P{\r a}l and Hugo Lewi {Hammer} and Riegler, Michael and Trine B. {Haugen}}
}
@inproceedings {hammer2019threat,
	title = {THREAT: A Large Annotated Corpus for Detection of Violent Threats},
	journal = {2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
	year = {2019},
	pages = {1{\textendash}5},
	publisher = {IEEE},
	author = {Hugo Lewi {Hammer} and Riegler, Michael and {\O}vrelid, Lilja and Velldal, Erik},
	editor = {Gurrin, Cathal}
}
@article {yazidi2019two,
	title = {Two-time scale learning automata: an efficient decision making mechanism for stochastic nonlinear resource allocation},
	journal = {Applied Intelligence},
	year = {2019},
	pages = {1{\textendash}14},
	publisher = {{Springer},
	author = {Yazidi, Anis and Hugo Lewi {Hammer} and Tore M {Jonassen}}
}
@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 {26205,
	title = {The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning},
	journal = {MediaEval 2018},
	year = {2018},
	month = {10/2018},
	publisher = {MediaEval},
	address = {Nice, France},
	abstract = {In this paper, we present our approach for the 2018 Medico Task classifying diseases in the gastrointestinal tract. We have proposed a system based on global features and deep neural networks. The best approach combines two neural networks and the reproducible experimental results signify the efficiency of the proposed model with an accuracy rate of 95.80\%, a precision of 95.87\%, and an F1-score of 95.80\%.},
	keywords = {CNN, deep learning, Gastrointestinal Disease Detection, Global Features, Medico-Task 2018, Transfer Learning},
	author = {Thambawita, Vajira and Jha, Debesh and Riegler, Michael and Halvorsen, P{\r a}l and Hugo Lewi {Hammer} and H{\r a}vard D. {Johansen} and Johansen, Dag}
}
