Blar i ODA Open Digital Archive på forfatter "Storås, Andrea"

Viser treff 1-8 av 8

    • Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure 

      Storås, Andrea; Riegler, Michael Alexander; Haugen, Trine B.; Thambawita, Vajira L B; Hicks, Steven Alexander; Hammer, Hugo Lewi; Kakulavarapu, Radhika; Halvorsen, Pål; Stensen, Mette Haug (Chapter; Conference object, 2023)
      The in vitro 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 ...

    • 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 ...

    • Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence 

      Storås, Andrea; Magnø, Morten Schjerven; Fineide, Fredrik; Thiede, Bernd; Chen, Xiangjun; Strumke, Inga; Halvorsen, Pål; Utheim, Tor Paaske; Riegler, Michael Alexander (Peer reviewed; Journal article, 2023)
      Meibomian gland dysfunction is the most common cause of dry eye disease and leads to significantly reduced quality of life and social burdens. Because meibomian gland dysfunction results in impaired function of the tear ...

    • Kvasir-VQA: A Text-Image Pair GI Tract Dataset 

      Gautam, Sushant; Storås, Andrea; Midoglu, Cise; Hicks, Steven; Thambawita, Vajira L B; Halvorsen, Pål; Riegler, Michael Alexander (Chapter; Peer reviewed; Conference object, 2024)
      We introduce Kvasir-VQA, an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with questionand-answer annotations to facilitate advanced machine learning tasks in Gastrointestinal (GI) ...

    • 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 ...

    • PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps 

      Fagereng, Jan Andre; Thambawita, Vajira L B; Storås, Andrea; Parasa, Sravanthi; de Lange, Thomas; Halvorsen, Pål; Riegler, Michael (Annual IEEE Symposium on Computer-Based Medical Systems;2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS), Conference object, 2022)
      Early identification of a polyp in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Developing computer-aided diagnosis (CAD) systems to detect polyps can improve detection ...

    • Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis 

      Storås, Andrea; Andersen, Ole Emil; Lockhart, Sam; Thielemann, Roman; Gnesin, Filip; Thambawita, Vajira L B; Hicks, Steven; Kanters, Jørgen K.; Strumke, Inga; Halvorsen, Pål; Riegler, Michael (Peer reviewed; Journal article, 2023)
      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 ...

    • Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations 

      Hicks, Steven; Storås, Andrea; Riegler, Michael; Midoglu, Cise; Hammou, Malek; Lange, Thomas de; Parasa, Sravanthi; Halvorsen, Pål; Strumke, Inga (Peer reviewed; Journal article, 2024)
      Deep learning has achieved immense success in computer vision and has the potential to help physicians analyze visual content for disease and other abnormalities. However, the current state of deep learning is very much a ...