Now showing items 201-220 of 483

    • Automatic algorithm for determining bone and soft-tissue factors in dual-energy subtraction chest radiography 

      Do, Quan; Seo, Wontaek; Shin, Choul Woo (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-11-11)
      Lung cancer is currently the first leading cause of worldwide cancer deaths since the early stage of lung cancer detection is still a challenge. In lung diagnosis, nodules sometimes overlap with ribs and tissues on lung chest radiographic images, which are complex for doctors and radiologists. Dual-energy subtraction (DES) is a suitable solution to solve those issues. This article will develop an ...
    • Designing, implementing, and testing a modern electronic clinical study management system – the HUBRO system 

      Muzny, Miroslav; Bradway, Meghan; Blixgård, Håvard Kvalvåg; Årsand, Eirik (Journal article; Tidsskriftartikkel, 2022-08-22)
      Clinical trials need to adapt to the rapid development of today’s digital health technologies. The fast phase these technologies are changing today, make the clinical study administration demanding. To meet this challenge, new and more efficient platforms for performing clinical trials in this domain need to be designed. Since the process of following up such trials is very time-consuming, it calls ...
    • 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 ...
    • H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images 

      Pedersen, André; Smistad, Erik; Rise, Tor Vikan; Dale, Vibeke Grotnes; Pettersen, Henrik P Sahlin; Nordmo, Tor-Arne Schmidt; Bouget, David Nicolas Jean-Mar; Reinertsen, Ingerid; Valla, Marit (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-09-14)
      Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded ...
    • A Pragmatic Machine Learning Approach to Quantify Tumor-Infiltrating Lymphocytes in Whole Slide Images 

      Shvetsov, Nikita; Grønnesby, Morten; Pedersen, Edvard; Møllersen, Kajsa; Rasmussen Busund, Lill-Tove; Schwienbacher, Ruth; Bongo, Lars Ailo; Kilvær, Thomas Karsten (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-06-16)
      Increased levels of tumor-infiltrating lymphocytes (TILs) indicate favorable outcomes in many types of cancer. The manual quantification of immune cells is inaccurate and time-consuming for pathologists. Our aim is to leverage a computational solution to automatically quantify TILs in standard diagnostic hematoxylin and eosin-stained sections (H&E slides) from lung cancer patients. Our approach ...
    • 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 (Conference object; Konferansebidrag, 2019-10-21)
      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 amount of generated data is often visualized in graphs and dashboards, for use by coaches and other sports professionals to make decisions on training and match strategies. Modern ...
    • On Edge Cloud Service Provision with Distributed Home Servers 

      Khan, Muhammed Amin; Freitag, Felix (Conference object; Konferansebidrag, 2017-12-28)
      Edge computing has been proposed for new types of cloud services, which need computing infrastructure at the network edge. Driven by important use cases from the Internet of Things (IoT) domain, edge cloud computing has also a huge business potential. Edge computing devices are already operational in many industrial and consumer-oriented scenarios. A typical characteristic of these solutions is, ...
    • IT2-GSETSK: An evolving interval Type-II TSK fuzzy neural system for online modeling of noisy data 

      Ashrafi, Mohammad; Prasad, Dilip K.; Quek, Chai (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-05-12)
      As a core part of a fuzzy neural system, the rule base antecedents and consequents may carry uncer- tainties because they are trained using noisy data. So, handling the uncertain rule base is an important need in some specific problems such as noisy non-dynamic problems which leads a better data model- ing. As a solution, Interval Type-II (IT2) version of GSETSK (Generic Self-Evolving ...
    • 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)
    • A Generic Undo Support for State-Based CRDTs 

      Yu, Weihai; Elvinger, Victorien; Ignat, Claudia-Lavinia (Chapter; Bokkapittel, 2020-02-11)
      CRDTs (Conflict-free Replicated Data Types) have properties desirable for large-scale distributed systems with variable network latency or transient partitions. With CRDT, data are always available for local updates and data states converge when the replicas have incorporated the same updates. Undo is useful for correcting human mistakes and for restoring system-wide invariant violated due to long ...
    • Unified Power Control of Permanent Magnet Synchronous Generator Based Wind Power System with Ancillary Support during Grid Faults 

      Ramachandran, Vijayapriya; Sendraya Perumal, Angalaeswari; Lakshmaiya, Natrayan; Paramasivam, Prabhu; Dhanasekaran, Seshathiri (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-10-08)
      A unified active power control scheme is devised for the grid-integrated permanent magnet synchronous generator-based wind power system (WPS) to follow the Indian electricity grid code requirements. The objective of this paper is to propose control schemes to ensure the continuous integration of WPS into the grid even during a higher percentage of voltage dip. In this context, primarily a constructive ...
    • Visual Sentiment Analysis from Disaster Images in Social Media 

      Zohaib Hassan, Syed; Ahmad, Kashif; Hicks, Steven; Halvorsen, Pål; Al-Fuqaha, Ala; Conci, Nicola; Riegler, Michael Alexander (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-05-10)
      The increasing popularity of social networks and users’ tendency towards sharing their feelings, expressions, and opinions in text, visual, and audio content have opened new opportunities and challenges in sentiment analysis. While sentiment analysis of text streams has been widely explored in the literature, sentiment analysis from images and videos is relatively new. This article focuses on ...
    • Uncertainty Estimation and Visualization of Wind in Weather Forecasts 

      Fjukstad, Bård; Bjørndalen, John Markus; Anshus, Otto (Chapter; Bokkapittel, 2014-01)
      The Collaborative Symbiotic Weather Forecasting system, CSWF, let individual users do on-demand small region, short-term, and very high-resolution forecasts. When the regions have some overlap, a symbiotic forecast can be produced based on the individual forecasts from each region. Small differences in where the center of the region is located when there is complex terrain in the region, leads to ...
    • 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 ...
    • A surrogate-assisted measurement correction method for accurate and low-cost monitoring of particulate matter pollutants 

      Wojcikowski, Marek; Pankiewicz, Bogdan; Bekasiewicz, Adrian; Cao, Tuan-Vu; Lepioufle, Jean-Marie; Vallejo, Islen; Ødegård, Rune Åvar; Ha, Hoai Phuong (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-07-12)
      Air pollution involves multiple health and economic challenges. Its accurate and low-cost monitoring is important for developing services dedicated to reduce the exposure of living beings to the pollution. Particulate matter (PM) measurement sensors belong to the key components that support operation of these systems. In this work, a modular, mobile Internet of Things sensor for PM measurements has ...
    • WANTED! – Virtual Coach for People with Thorny Diseases 

      Halonen, Raija; Savenstedt, Stefan; Hartvigsen, Gunnar; Abächerli, Roger; Jääskeläinen, Erika; Synnes, Kåre (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-01-03)
      The main objective of this study was to propose a concept for a virtual coach to be used by people who suffer from costly and challenging diseases such as dementia, depression, diabetes and cardiac related issues, and by their caretakers presenting healthcare service providers or family members of the people suffering from the named diseases. Those listed diseases form almost an unbearable ...
    • Video Analytics in Elite Soccer: A Distributed Computing Perspective 

      Jha, Debesh; Rauniyar, Ashish; Johansen, Håvard D.; Johansen, Dag; Riegler, Michael Alexander; Halvorsen, Pål; Bagci, Ulas (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-07-22)
      Ubiquitous sensors and Internet of Things (IoT)technologies have revolutionized the sports industry, providing new methodologies for planning, effective coordination of training, and match analysis post-game. New methods, including machine learning, image, and video processing, have been developed for performance evaluation, allowing the analyst to track the performance of a player in real-time. ...
    • Standards for reporting randomized controlled trials in medical informatics: a systematic review of CONSORT adherence in RCTs on clinical decision support 

      Augestad, Knut Magne; Berntsen, Gro; Lassen, Kristoffer; Bellika, Johan Gustav; Wootton, Richard; Lindsetmo, Rolv-Ole (Journal article; Tidsskriftartikkel; Peer reviewed, 2011-07-29)
      Introduction The Consolidated Standards for Reporting Trials (CONSORT) were published to standardize reporting and improve the quality of clinical trials. The objective of this study is to assess CONSORT adherence in randomized clinical trials (RCT) of disease specific clinical decision support (CDS).<p> <p>Methods A systematic search was conducted of the Medline, EMBASE, and Cochrane databases. ...
    • Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources 

      Eide, Siri Sofie; Riegler, Michael; Hammer, Hugo Lewi; Bremnes, John Bjørnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-04-06)
      Many data related problems involve handling multiple data streams of different types at the same time. These problems are both complex and challenging, and researchers often end up using only one modality or combining them via a late fusion based approach. To tackle this challenge, we develop and investigate the usefulness of a novel deep learning method called tower networks. This method is able ...
    • Efficient quantile tracking using an oracle 

      Hammer, Hugo Lewi; Yazidi, Anis; Riegler, Michael; Rue, Håvard (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-04-14)
      Concept drift is a well-known issue that arises when working with data streams. In this paper, we present a procedure that allows a quantile tracking procedure to cope with concept drift. We suggest using expected quantile loss, a popular loss function in quantile regression, to monitor the quantile tracking error, which, in turn, is used to efficiently adapt to concept drift. The suggested ...