• AI-based clipping of booking events in soccer 

      Rognerud, Robin (Master thesis, 2023)
      Manual clipping is currently the gold standard for extracting highlight clips from soccer games. However, it is a costly, tedious, and time-consuming task that is impractical and unfeasible for, at least, lower-league games ...
    • Artificial intelligence in the fertility clinic: status, pitfalls and possibilities 

      Riegler, Michael Alexander; Stensen, Mette Haug; Witczak, Oliwia; Andersen, Jorunn Marie; Hicks, Steven; Hammer, Hugo Lewi; Delbarre, Erwan; Halvorsen, Pål; Yazidi, Anis; Holst, Nicolai; Haugen, Trine B. (Human Reproduction;Volume 36, Issue 9, Peer reviewed; Journal article, 2021-07-29)
      In recent years, the amount of data produced in the field of assisted reproduction technology [ART] has increased exponentially. The diversity of data is large, ranging from videos to tabular data. At the same time, ...
    • Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge 

      Ali, Sharib; Ghatwary, Noha; Jha, Debesh; Isik-Polat, Ece; Polat, Gorkem; Yang, Cheng; Li, Wuyang; Galdran, Adrian; Ballester, Miguel Angel Gonzalez; Thambawita, Vajira L B; Hicks, Steven; Poudel, Sahadev; Lee, Sang-Woong; Jin, Ziyi; Gan, Tianyuan; Yu, Chenghui; Yan, JiangPeng; Yeo, Doyeob; Lee, Hyunseok Lee; Tomar, Nikhil Kumar; Haitham, Mahmood; Ahmed, Amr; Riegler, Michael Alexander; Daul, Christian; Halvorsen, Pål; Rittscher, Jens; Salem, Osama E.; Lamarque, Dominique; Cannizzaro, Renato; Realdon, Stefano; de Lange, Thomas; East, James E (Peer reviewed; Journal article, 2024)
      Polyps are well‑known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal ...
    • Automated reporting system using deep convolutional neural network in the medical domain 

      Subedi, Matrika (MAUU;2021, Master thesis, 2021)
      Nowadays, in the healthcare sector, a massive volume of medical data sources is available. The data is growing at 153 Exabytes in 2013 and an estimated 2,314 exabytes in 2020 (Turner, Gantz et al. 2014). The medical data ...
    • CELLULAR, A Cell Autophagy Imaging Dataset 

      al Outa, Amani; Hicks, Steven; Thambawita, Vajira L B; Andresen, Siri; Enserink, Jorrit Martijn; Halvorsen, Pål; Riegler, Michael Alexander; Knævelsrud, Helene (Peer reviewed; Journal article, 2023)
      Cells in living organisms are dynamic compartments that continuously respond to changes in their environment to maintain physiological homeostasis. While basal autophagy exists in cells to aid in the regular turnover of ...
    • Comparing Recurrent Neural Networks for ECG analysis 

      Sæther, Sander (Master thesis, 2023)
      In this thesis, the effectiveness of three types of Recurrent Neural Networks (RNNs) - Basic RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) - are examined for Electrocardiogram (ECG) signal classification. ...
    • A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging 

      Jha, Debesh; Ali, Sharib; Hicks, Steven; Thambawita, Vajira L B; Borgli, Hanna; Smedsrud, Pia H.; de Lange, Thomas; Pogorelov, Konstantin; Wang, Xiaowei; Harzig, Philipp; Tran, Minh-Triet; Meng, Wenhua; Hoang, Trung-Hieu; Dias, Danielle; Ko, Tobey H.; Agrawal, Taruna; Ostroukhova, Olga; Khan, Zeshan; Tahir, Muhammed Atif; Liu, Yang; Chang, Yuan; Kirkerød, Mathias; Johansen, Dag; Lux, Mathias; Johansen, Håvard D.; Riegler, Michael; Halvorsen, Pål (Peer reviewed; Journal article, 2021)
      Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed ...
    • A dataset for predicting cloud cover over Europe 

      Svennevik, Hanna; Hicks, Steven; Riegler, Michael; Storelvmo, Trude; Hammer, Hugo Lewi (Peer reviewed; Journal article, 2024)
      Clouds are important factors when projecting future climate. Unfortunately, future cloud fractional cover (the portion of the sky covered by clouds) is associated with significant uncertainty, making climate projections ...
    • DivergentNets: Medical Image Segmentation by Network Ensemble 

      Thambawita, Vajira L B; Hicks, Steven; Halvorsen, Pål; Riegler, Michael (CEUR Workshop Proceedings;Vol-2886 - Proceedings of the 3rd International Workshop and Challenge on Computer Vision in Endoscopy (EndoCV 2021), Conference object, 2021)
      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 ...
    • An evaluation of using transformer networks for ECG Analysis 

      Yawar, Syeda Ambreen (Master thesis, 2023)
      Electrocardiogram (ECG) is a simulated recording of heart activity in electrical signals. It carries essential clinical information in the form of amplitude and timing. It is used to monitor and analyze the functionality ...
    • Explaining deep neural networks for knowledge discovery in electrocardiogram analysis 

      Hicks, Steven; Isaksen, Jonas L; Thambawita, Vajira L B; Ghouse, Jonas; Ahlberg, Gustav; Linneberg, Allan; Grarup, Niels; Strumke, Inga; Ellervik, Christina; Olesen, Morten Salling; Hansen, Torben; Graff, Claus; Holstein-Rathlou, Niels-Henrik; Halvorsen, Pål; Maleckar, Mary Margot Catherine; Riegler, Michael; Kanters, Jørgen K (Scientific Reports;11, Article number: 10949 (2021), Peer reviewed; Journal article, 2021-05-26)
      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 ...
    • Huldra: a framework for collecting crowdsourced feedback on multimedia assets 

      Hammou, Malek; Midoglu, Cise; Hicks, Steven; Storås, Andrea; Sabet, Saeed; Strumke, Inga; Riegler, Michael; Halvorsen, Pål (MMSys: ACM Multimedia Systems;MMSys '22: Proceedings of the 13th ACM Multimedia Systems Conference, Conference object, 2022)
      Collecting crowdsourced feedback to evaluate, rank, or score multimedia content can be cumbersome and time-consuming. Most of the existing survey tools are complicated, hard to customize, or tailored for a specific asset ...
    • HYPERAKTIV: An Activity Dataset from Patients with Attention-Deficit/Hyperactivity Disorder (ADHD) 

      Hicks, Steven; Stautland, Andrea; Fasmer, Ole Bernt; Førland, Wenche; Hammer, Hugo Lewi; Halvorsen, Pål; Mjeldheim, Kristin; Ødegaard, Ketil Joachim; Osnes, Berge; Syrstad, Vigdis Elin Giæver; Riegler, Michael; Jakobsen, Petter (MMSys: ACM Multimedia Systems;MMSys '21: Proceedings of the 12th ACM Multimedia Systems Conference, Conference object, 2021-09-22)
      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 requirementsthat come with ...
    • HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy 

      Borgli, Hanna; Thambawita, Vajira; Smedsrud, Pia H; Hicks, Steven; Jha, Debesh; Eskeland, Sigrun Losada; Randel, Kristin Ranheim; Pogorelov, Konstantin; Lux, Mathias; Dang Nguyen, Duc Tien; Johansen, Dag; Griwodz, Carsten; Stensland, Håkon Kvale; Garcia-Ceja, Enrique; Schmidt, Peter T; Hammer, Hugo Lewi; Riegler, Michael; Halvorsen, Pål; de Lange, Thomas (Scientific Data;7, Article number: 283 (2020), Journal article; Peer reviewed, 2020-08-28)
      Artifcial 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 ...
    • Impact of image resolution on deep learning performance in endoscopy image classification: An experimental study using a large dataset of endoscopic images 

      Thambawita, Vajira L B; Strumke, Inga; Hicks, Steven; Halvorsen, Pål; Parasa, Sravanthi; Riegler, Michael (Diagnostics;Volume 11 / Issue 12, Peer reviewed; Journal article, 2021-11-24)
      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 ...
    • An Investigation into using Deep Convolutional Neural Networks for ECG Analysis 

      Hameed, Mohammad Awais (Master thesis, 2023)
      In this day and age, the fascination surrounding deep learning and AI is at its absolute peak. Both in terms of hype and controversy the current interest level is unprecedented, with exciting developments happening at a ...
    • Kvasir-Capsule, a video capsule endoscopy dataset 

      Smedsrud, Pia H; Thambawita, Vajira L B; Hicks, Steven; Gjestang, Henrik; Olsen Nedrejord, Oda; Næss, Espen; Borgli, Hanna; Jha, Debesh; Berstad, Tor Jan; Eskeland, Sigrun Losada; Lux, Mathias; Espeland, Håvard; Petlund, Andreas; Dang Nguyen, Duc Tien; Garcia, Enrique; Johansen, Dag; Schmidt, Peter Thelin; Toth, Ervin; Hammer, Hugo Lewi; de Lange, Thomas; Riegler, Michael Alexander; Halvorsen, Pål (Scientific Data;8, Article number: 142 (2021), Peer reviewed; Journal article, 2021-05-27)
      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 ...
    • Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction 

      Hicks, Steven; Andersen, Jorunn Marie; Witczak, Oliwia; Lasantha Bandara Thambawita, Vajira; Halvorsen, Pål; Hammer, Hugo Lewi; Haugen, Trine B.; Riegler, Michael Alexander (Scientific Reports;9, Article number: 16770 (2019), Journal article; Peer reviewed, 2019-10-24)
      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 data are ...
    • 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 (Computers in Biology and Medicine;Volume 143, April 2022, 105227, Peer reviewed; Journal article, 2022-02-03)
      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 ...
    • Multimedia Datasets: Challenges and Future Possibilities 

      Nguyen, Thu; Storås, Andrea; Thambawita, Vajira L B; Hicks, Steven; Halvorsen, Pål; Riegler, Michael Alexander (Chapter; Peer reviewed; Conference object; Journal article, 2023)
      Public multimedia datasets can enhance knowledge discovery and model development as more researchers have the opportunity to contribute to exploring them. However, as these datasets become larger and more multimodal, besides ...