Blar i forfatter "Riegler, Michael Alexander"

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    • Artificial Intelligence for Colonoscopy: Past, Present, and Future 

      Tavanapong, Wallapak; Oh, Junghwan; Riegler, Michael Alexander; Khaleel, Mohammed; Mittal, Bhuvan; de Groen, Piet (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-03-22)
      During the past decades, many automated image analysis methods have been developed for colonoscopy. Realtime implementation of the most promising methods during colonoscopy has been tested in clinical trials, including several recent multi-center studies. All trials have shown results that may contribute to prevention of colorectal cancer. We summarize the past and present development of ...

    • Artificial intelligence in dry eye disease 

      Storås, Andrea Marheim; Strumke, Inga; Riegler, Michael Alexander; Grauslund, Jakob; Hammer, Hugo Lewi; Yazidi, Anis; Halvorsen, Pål; Gundersen, Kjell Gunnar; Utheim, Tor Paaske; Jackson, Catherine Joan (Journal article; Tidsskriftartikkel, 2021-11-27)
      Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. ...

    • 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. (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-07-29)
      In recent years, the amount of data produced in the field of ART has increased exponentially. The diversity of data is large, ranging from videos to tabular data. At the same time, artificial intelligence (AI) is progressively used in medical practice and may become a promising tool to improve success rates with ART. AI models may compensate for the lack of objectivity in several critical procedures ...

    • 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; Bokkapittel, 2023-02-02)
      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 improve the method in the fertility clinic, the procedure is usually observed at the clinic. Alternatively, a video of the procedure can be examined and labeled in a time-consuming ...

    • 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 (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-11-16)
      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 intracellular material, autophagy is also a critical cellular response to stress, such as nutritional depletion. Conversely, the deregulation of autophagy is linked to several diseases, ...

    • A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation 

      Jha, Debesh; Smedsrud, Pia; Johansen, Dag; de Lange, Thomas; Johansen, Håvard D.; Halvorsen, Pål; Riegler, Michael Alexander (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-01-05)
      Colonoscopy is considered the gold standard for detection of colorectal cancer and its precursors. Existing examination methods are, however, hampered by high overall miss-rate, and many abnormalities are left undetected. Computer-Aided Diagnosis systems based on advanced machine learning algorithms are touted as a game-changer that can identify regions in the colon overlooked by the physicians ...

    • Deep learning neural network can measure ECG intervals and amplitudes accurately 

      Kanters, Jørgen K.; Hicks, Steven; Isaksen, Jonas L; Grarup, Niels; Holstein-Rathlou, Niels-Henrik; Ghouse, Jonas; Ahlberg, Gustav; Olesen, Morten Salling; Linneberg, Allan; Ellervik, Christina; Hansen, Torben; Graff, Claus; Halvorsen, Pål; Riegler, Michael Alexander (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-12-03)

    • DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation 

      Jha, Debesh; Riegler, Michael Alexander; Johansen, Dag; Halvorsen, Pål; Johansen, Håvard D. (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-09-01)
      Semantic image segmentation is the process of labeling each pixel of an image with its corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a popular strategy for solving medical image segmentation tasks. To improve the performance of U-Net on various segmentation tasks, we propose a novel architecture called DoubleU-Net, which is a combination of two U-Net ...

    • Enhancing questioning skills through child avatar chatbot training with feedback 

      Røed, Ragnhild Klingenberg; Baugerud, Gunn Astrid; Zohaib Hassan, Syed; Shafiee Sabet, Saeed; Salehi, Pegah; Powell, Martine B.; Riegler, Michael Alexander; Halvorsen, Pål; Sinkerud Johnson, Miriam (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-07-13)
      Training child investigative interviewing skills is a specialized task. Those being trained need opportunities to practice their skills in realistic settings and receive immediate feedback. A key step in ensuring the availability of such opportunities is to develop a dynamic, conversational avatar, using artificial intelligence (AI) technology that can provide implicit and explicit feedback to ...

    • File System Support for Privacy-Preserving Analysis and Forensics in Low-Bandwidth Edge Environments 

      Ovesen, Aril Bernhard; Nordmo, Tor-Arne Schmidt; Johansen, Håvard D.; Riegler, Michael Alexander; Halvorsen, Pål; Johansen, Dag (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-10-18)
      In this paper, we present initial results from our distributed edge systems research in the domain of sustainable harvesting of common good resources in the Arctic Ocean. Specifically, we are developing a digital platform for real-time privacy-preserving sustainability management in the domain of commercial fishery surveillance operations. This is in response to potentially privacy-infringing mandates ...

    • Fish AI: Sustainable Commercial Fishing Challenge 

      Nordmo, Tor-Arne Schmidt; Kvalsvik, Ove; Kvalsund, Svein Ove; Hansen, Birte; Halvorsen, Pål; Hicks, Steven; Johansen, Dag; Johansen, Håvard D.; Riegler, Michael Alexander (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-06-02)
      FishAI: Sustainable Commercial Fishingis the second chal-lenge at theNordic AI Meetfollowing the successful MedAI,which had a focus on medical image segmentation and trans-parency in machine learning (ML)-based systems. FishAI fo-cuses on a new domain, namely, commercial fishing and howto make it more sustainable with the help of machine learning.A range of public available datasets is used to tackle ...

    • Fishing Trawler Event Detection: An Important Step Towards Digitization of Sustainable Fishing 

      Nordmo, Tor-Arne Schmidt; Ovesen, Aril Bernhard; Dagenborg, Håvard; Halvorsen, Pål; Riegler, Michael Alexander; Johansen, Dag (Chapter; Bokkapittel, 2023-08-02)
      Detection of anomalies within data streams is an important task that is useful for different important societal challenges such as in traffic control and fraud detection. To be able to perform anomaly detection, unsupervised analysis of data is an important key factor, especially in domains where obtaining labelled data is difficult or where the anomalies that should be detected are often ...

    • Generation of synthetic tabular healthcare data using generative adversarial networks 

      Nik, Alireza Hossein Zadeh; Riegler, Michael Alexander; Halvorsen, Pål; Storås, Andrea (Chapter; Bokkapittel, 2023-03-29)
      High-quality tabular data is a crucial requirement for developing data-driven applications, especially healthcare-related ones, because most of the data nowadays collected in this context is in tabular form. However, strict data protection laws complicates the access to medical datasets. Thus, synthetic data has become an ideal alternative for data scientists and healthcare professionals to circumvent ...

    • 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; Jensen, Janicke L.; Galtung, Hilde (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-07-17)
      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 film lipid layer, studying the expression of tear proteins might increase the understanding of the etiology of the condition. Machine learning is able to detect patterns in ...

    • 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 (Journal article; Tidsskriftartikkel; Peer reviewed, 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 demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, ...

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

      Jha, Debesh; Yazidi, Anis; Riegler, Michael Alexander; Johansen, Dag; Johansen, Håvard D.; Halvorsen, Pål (Chapter; Bokkapittel, 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. Organizations or individuals that cannot afford purchasing state-of-the-art hardware or tapping into cloud hosted infrastructures may face a long waiting time before the training ...

    • 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 (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-02-06)
      Polyps in the colon are widely known cancer precursors identifed by colonoscopy. Whilst most polyps are benign, the polyp’s number, size and surface structure are linked to the risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicentre purpose-built dataset, one reason ...

    • 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; Bokkapittel, 2022-08-05)
      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 research from different fields like marine biology, fishery sciences, cybernetics, and computer science. In computer science, detection of fishing vessels via for example remote ...

    • 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 (Journal article; Tidsskriftartikkel; Peer reviewed, 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 are typically reported to summarize a model’s performance. Unfortunately, these measures are not easily understandable by many clinicians. Moreover, comparison of models across studies ...

    • Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence 

      Fineide, Fredrik; Storås, Andrea; Riegler, Michael Alexander; Utheim, Tor Paaske (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-07-17)
      Dry eye disease is a common and potentially debilitating medical condition. Meibum secreted from the meibomian glands is the largest contributor to the outermost, protective lipid layer of the tear film. Dysfunction of the meibomian glands is the most common cause of dry eye disease. As meibomian gland dysfunction progresses, gradual atrophy of the glands is observed. The meibomian glands are ...