Browsing TKD - Institutt for informasjonsteknologi by Author "Halvorsen, Pål"

Now showing items 21-40 of 60

    • 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 ...

    • 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 ...

    • 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 ...

    • 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 ...

    • Kvasir-SEG: A Segmented Polyp Dataset 

      Jha, Debesh; Pia H, Smedsrud; Riegler, Michael; Halvorsen, Pål; de Lange, Thomas; Johansen, Dag; Johansen, Håvard D. (Lecture Notes in Computer Science;Volume 11962, Conference object, 2019-12-24)
      Pixel-wise image segmentation is a highly demanding task in medical-image analysis. In practice, it is difficult to find annotated medical images with corresponding segmentation masks. In this paper, we present Kvasir-SEG: ...

    • LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification 

      Jha, Debesh; Yazidi, Anis; Riegler, Michael Alexander; Johansen, Dag; Johansen, Håvard D.; Halvorsen, Pål (Lecture Notes in Computer Science;Volume 12606, Conference object, 2021-02-21)
      Deep Neural Networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks. A key drawback of DNNs is that the training phase can be very computationally expensive. ...

    • 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 ...

    • Man vs. AI: An in silico study of polyp detection performance 

      Smedsrud, Pia Helen; Espeland, Håvard; Berstad, Tor Jan; Petlund, Andreas; de Lange, Thomas; Riegler, Michael; Halvorsen, Pål (Annual IEEE Symposium on Computer-Based Medical Systems;, Chapter; Peer reviewed; Conference object; Journal article, 2023)
      AI-based colon polyp detection systems have received much attention, and several products and prototypes report good results. In silico verification is a crucial step when developing such systems, but very few compare human ...

    • Mask-conditioned latent diffusion for generating gastrointestinal polyp images 

      Macháček, Roman; Mozaffari, Leila; Sepasdar, Zahra; Parasa, Sravanthi; Halvorsen, Pål; Riegler, Michael; Thambawita, Vajira L B; Machacek, Roman (ICDAR: Intelligent Cross-Data Analysis and Retrieval;, Chapter; Peer reviewed; Conference object, 2023)
      In order to take advantage of artificial intelligence (AI) solutions in endoscopy diagnostics, we must overcome the issue of limited annotations. These limitations are caused by the high privacy concerns in the medical ...

    • 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 ...

    • MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation 

      Srivastava, Abhishek; Jha, Debesh; Chanda, Sukalpa; Pal, Umapada; Johansen, Håvard D.; Johansen, Dag; Riegler, Michael; Ali, Sharib; Halvorsen, Pål (IEEE journal of biomedical and health informatics;Volume: 26, Issue: 5, Peer reviewed; Journal article, 2021-12-23)
      Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of variable sizes and train on small and ...

    • A multi-centre polyp detection and segmentation dataset for generalisability assessment 

      Ali, Sharib; Jha, Debesh; Ghatwary, Noha; Realdon, Stefano; Cannizzaro, Renato; Salem, Osama E.; Lamarque, Dominique; Daul, Christian; Riegler, Michael Alexander; Ånonsen, Kim Vidar; Petlund, Andreas; Halvorsen, Pål; Rittscher, Jens; de Lange, Thomas; East, James E (Peer reviewed; Journal article, 2023)

    • 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 ...

    • Multimedia streaming analytics: quo vadis? 

      Midoglu, Cise; Avelino, Mariana; Gopalakrishnan, Shri Hari; Pham, Stefan; Halvorsen, Pål (MHV: Mile-High Video;MHV '22: Proceedings of the 1st Mile-High Video Conference, Conference object, 2022)
      In today’s complex OTT multimedia streaming ecosystem, the task of ensuring the best streaming experience to end-users requires extensive monitoring, and such monitoring information is relevant to various stakeholders ...

    • Njord: a fishing trawler dataset 

      Nordmo, Tor-Arne Schmidt; Ovesen, Aril Bernhard; Juliussen, Bjørn Aslak; Hicks, Steven; Thambawita, Vajira L B; Johansen, Håvard D.; Halvorsen, Pål; Riegler, Michael Alexander; Johansen, Dag (Chapter, 2022)
      Fish is one of the main sources of food worldwide. The commercial fishing industry has a lot of different aspects to consider, ranging from sustainability to reporting. The complexity of the domain also attracts a lot of ...

    • 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 ...

    • Organiser Team at ImageCLEFlifelog 2020: A Baseline Approach for Moment Retrieval and Athlete Performance Prediction using Lifelog Data 

      Le, Tu-Khiem; Ninh, Van-Tu; Zhou, Liting; Nguyen-Ngoc, Minh-Huy; Piras, Luca; Riegler, Michael Alexander; Halvorsen, Pål; Lux, Mathias; Tran, Minh-Triet; Healy, Graham; Gurrin, Cathal; Dang Nguyen, Duc Tien (CEUR Workshop Proceedings;Volume 2696, Journal article; Peer reviewed, 2020)
      For the LMRT task at ImageCLEFlifelog 2020, LIFER 3.0, a new version of the LIFER system with improvements in the user in- terface and system affordance, is used and evaluated via feedback from a user experiment. In addition, ...

    • Overview of ImageCLEF Lifelog 2020: Lifelog Moment Retrieval and Sport Performance Lifelog 

      Ninh, Van-Tu; Le, Tu-Khiem; Zhou, Liting; Piras, Luca; Riegler, Michael Alexander; Halvorsen, Pål; Lux, Mathias; Tran, Minh-Triet; Gurrin, Cathal; Dang Nguyen, Duc Tien (CEUR Workshop Proceedings;Volume 2696, Journal article; Peer reviewed, 2020)
      This paper describes the fourth edition of Lifelog challenges in ImageCLEF 2020. In this edition, the Lifelog challenges consist of two tasks which are Lifelog Moments Retrieval (LMRT) and Sport Per- formance Lifelog (SPLL). ...