• DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine 

      Thambawita, Vajira; Isaksen, Jonas L.; Hicks, Steven A.; Ghouse, Jonas; Ahlberg, Gustav; Linneberg, Allan; Grarup, Niels; Ellervik, Christina; Olesen, Morten Salling; Hansen, Torben; Graff, Claus; Holstein-Rathlou, Niels-Henrik; Strümke, Inga; Hammer, Hugo L.; Maleckar, Mary M.; Halvorsen, Pål; Riegler, Michael A.; Kanters, Jørgen K. (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-11-09)
      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 ...
    • Meta-learning with implicit gradients in a few-shot setting for medical image segmentation 

      Khadka, Rabindra; Jha, Debesh; Riegler, Michael A.; Hicks, Steven; Thambawita, Vajira; Ali, Sharib; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-01-12)
      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 ...
    • Prediction of cloud fractional cover using machine learning 

      Svennevik, Hanna; Riegler, Michael A.; Hicks, Steven; Storelvmo, Trude; Hammer, Hugo L. (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-11-03)
      Climate change is stated as one of the largest issues of our time, resulting in many unwanted effects on life on earth. Cloud fractional cover (CFC), the portion of the sky covered by clouds, might affect global warming and different other aspects of human society such as agriculture and solar energy production. It is therefore important to improve the projection of future CFC, which is usually ...
    • Real-Time Detection of Events in Soccer Videos using 3D Convolutional Neural Networks 

      Rognved, Olav; Hicks, Steven; Lasantha Bandara Thambawita, Vajira; Stensland, Håkon Kvale; Zouganeli, Evi; Johansen, Dag; Riegler, Michael A.; Halvorsen, Pål (Chapter; Bokkapittel, 2020)
      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 Allsvenskan, and the Norwegian Eliteserien. ...
    • Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning 

      Jha, Debesh; Ali, Sharib; Tomar, Nikhil Kumar; Johansen, Håvard D.; Johansen, Dag; Rittscher, Jens; Riegler, Michael A.; Halvorsen, Pal (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-03-04)
      Computer-aided detection, localization, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer vision methods that can be applied to polyp datasets. Benchmarking ...