Browsing TKD - Institutt for informasjonsteknologi by Author "Hammou, Malek"

Now showing items 1-4 of 4

    • HOST-ATS: automatic thumbnail selection with dashboard-controlled ML pipeline and dynamic user survey 

      Husa, Andreas; Midoglu, Cise; Hammou, Malek; Halvorsen, Pål; Riegler, Michael (MMSys: ACM Multimedia Systems;MMSys '22: Proceedings of the 13th ACM Multimedia Systems Conference, Conference object, 2022)
      We present HOST-ATS, a holistic system for the automatic selection and evaluation of soccer video thumbnails, which is composed of a dashboard-controlled machine learning (ML) pipeline, and a dynamic user survey. The ML ...

    • 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 ...

    • On evaluation metrics for medical applications of artificial intelligence 

      Hicks, Steven A.; Strumke, Inga; Thambawita, Vajira L B; Hammou, Malek; Riegler, Michael Alexander; Halvorsen, Pål (Scientific Reports;12, Article number: 5979 (2022), Peer reviewed; Journal article, 2022-04-08)
      Clinicians and software developers need to understand how proposed machine learning (ML) models could improve patient care. No single metric captures all the desirable properties of a model, which is why several metrics ...

    • 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 ...