Browsing ODA Open Digital Archive by Author "Riegler, Michael"

Now showing items 21-40 of 47

    • 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: ...

    • 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 ...

    • 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 ...

    • Performance of data enhancements and training optimization for neural network: A polyp detection case study 

      Henriksen, Fredrik Lund; Jensen, Rune; Stensland, Håkon Kvale; Johansen, Dag; Riegler, Michael; Halvorsen, Pål (IEEE International Symposium on Computer-Based Medical Systems; 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), Conference object, 2019)
      Deep learning using neural networks is becoming more and more popular. It is frequently used in areas like video analysis, image retrieval, traffic forecast and speech recognition. In this respect, the learning and training ...

    • PMData: a sports logging dataset 

      Thambawita, Vajira; Hicks, Steven; Borgli, Hanna; Stensland, Håkon Kvale; Jha, Debesh; Svensen, Martin Kristoffer; Pettersen, Svein Arne; Johansen, Dag; Johansen, Håvard D.; Pettersen, Susann Dahl; Nordvang, Simon; Pedersen, Sigurd; Gjerdrum, Anders Tungeland; Grønli, Tor-Morten; Fredriksen, Per Morten; Eg, Ragnhild; Hansen, Kjeld S.; Fagernes, Siri; Claudi, Christine; Biørn-Hansen, Andreas; Dang Nguyen, Duc Tien; Kupka, Tomas; Hammer, Hugo Lewi; Jain, Ramesh; Riegler, Michael; Halvorsen, Pål (MM: International Multimedia Conference;MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference, Conference object, 2020-05-27)
      In this paper, we present PMData: a dataset that combines traditional lifelogging data with sports-activity data. Our dataset enables the development of novel data analysis and machine-learning applications where, for ...

    • 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 ...

    • Polyps segmentation using synthetic images generated by GAN 

      Fagereng, Jan André (ACIT;2022, Master thesis, 2022)
      Early identification of polyps in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Multiple studies have shown that up to 28% of polyps might be missed during ...

    • Predicting Cell Cleavage Timings from Time-Lapse Videos of Human Embryos 

      Sharma, Akriti; Ansari, Ayaz Z.; Kakulavarapu, Radhika; Stensen, Mette Haug; Riegler, Michael; Hammer, Hugo Lewi (Peer reviewed; Journal article, 2023)
      Assisted reproductive technology is used for treating infertility, and its success relies on the quality and viability of embryos chosen for uterine transfer. Currently, embryologists manually assess embryo development, ...

    • Predicting High Delays in Mobile Broadband Networks 

      Mohamed Ahmed, Azza Hassan; Hicks, Steven; Riegler, Michael; Elmokashfi, Ahmed Mustafa Abdalla (IEEE Access;Volume 9: 2021, Peer reviewed; Journal article, 2021-12-24)
      The number of applications that run over mobile networks, expecting bounded end-to-end delay, is increasing steadily. However, the stochastic and shared nature of the wireless medium makes providing such guarantees ...

    • Predicting peek readiness-to-train of soccer players using long short-term memory recurrent neural networks 

      Wiik, Theodor; Johansen, Håvard D.; Pettersen, Svein Arne; Matias Do Vale Baptista, Ivan Andre; Kupka, Tomas; Johansen, Dag; Riegler, Michael; Halvorsen, Pål (International Workshop on Content-Based Multimedia Indexing, CBMI;, Conference object, 2019)
      We are witnessing the emergence of a myriad of hardware and software systems that quantifies sport and physical activities. These are frequently touted as game changers and important for future sport developments. The vast ...

    • Prediction of schizophrenia from activity data using hidden Markov model parameters 

      Boeker, Matthias; Hammer, Hugo Lewi; Riegler, Michael; Halvorsen, Pål; Jakobsen, Petter (Peer reviewed; Journal article, 2022)

    • 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; Halvorsen, Pål (IEEE Access;Volume: 9, 2021, Peer reviewed; Journal article, 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 ...

    • ResUNet++: An Advanced Architecture for Medical Image Segmentation 

      Jha, Debesh; Smedsrud, Pia; Riegler, Michael; Johansen, Dag; de Lange, Thomas; Halvorsen, Pål; Johansen, Håvard D. (IEEE International Symposium on Multimedia; 2019 IEEE International Symposium on Multimedia (ISM), Conference object, 2020-01-16)
      Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer. Towards developing a fully ...

    • A Robust Framework for Distributional Shift Detection Under Sample-Bias 

      Torpmann-Hagen, Birk Sebastian Frostelid; Riegler, Michael; Halvorsen, Pål; Johansen, Dag (Peer reviewed; Journal article, 2024)
      Deep Neural Networks have been shown to perform poorly or even fail altogether when deployed in real-world settings, despite exhibiting excellent performance on initial benchmarks. This typically occurs due to relative ...

    • A Salutogenic Approach for Collaboration in Health and Technology 

      Berg, Arild Skarsfjord; Johansen, Safora; Lund, Anne; Riegler, Michael; Andersen, Jorunn Marie (Chapter, 2023)
      Through collaboration, health services and health-promoting environments can be influenced by patients, health professionals, and stakeholders. Antonovsky’s concept of salutogenesis includes the promotion of a sense of ...

    • Semantic Analysis of Soccer News for Automatic Game EventClassification 

      Nordskog, Aanund Jupskaas; Halvorsen, Pål; Hicks, Steven; Stensland, Haakon; Hammer, Hugo Lewi; Johansen, Dag; Riegler, Michael (International Workshop on Content-Based Multimedia Indexing, CBMI; 2019 International Conference on Content-Based Multimedia Indexing (CBMI), Conference object, 2019-10-21)
      We are today overwhelmed with information, of which an important part is news. Sports news, in particular, has become very popular, where soccer makes up a big part of this coverage. For sports fans, it can be a time ...

    • SinGAN-Seg: Synthetic training data generation for medical image segmentation 

      Thambawita, Vajira L B; Salehi, Pegah; Sheshkal, Sajad Amouei; Hicks, Steven; Hammer, Hugo Lewi; Parasa, Sravanthi; de Lange, Thomas; Halvorsen, Pål; Riegler, Michael (PLOS ONE;17(5): e0267976, Peer reviewed; Journal article, 2022-05-02)
      Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Therefore, artificial intelligence has become a ...

    • Smittestopp analytics: Analysis of position data 

      Thambawita, Vajira L B; Hicks, Steven; Ewan, Jaouen; Halvorsen, Pål; Riegler, Michael (Simula SpringerBriefs on Computing;Volume 11, Chapter; Peer reviewed, 2022-06-18)
      Contact tracing applications generally rely on Bluetooth data. This type of data works well to determine whether a contact occurred (smartphones were close to each other) but cannot offer the contextual information GPS ...

    • Soccer athlete performance prediction using time series analysis 

      Ragab, Nourhan (ACIT;2022, Master thesis, 2022)
      Regardless of the sport you prefer, your favorite athlete has almost certainly disappointed you at some point. Did you jump to a conclusion and dismissed it as "not their day"? Or, did you consider the underlying causes for ...