Blar i ODA Open Digital Archive på forfatter "Thambawita, Vajira"

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    • Comparing Recurrent Neural Networks for ECG analysis 

      Sæther, Sander (Master thesis, 2023)
      In this thesis, the effectiveness of three types of Recurrent Neural Networks (RNNs) - Basic RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) - are examined for Electrocardiogram (ECG) signal classification. ...

    • Conditional Deep Generative Models for Generating Synthetic Electrocardiograms 

      Upreti, Ramesh (Master thesis, 2023)
      Using artificial intelligence (AI)-based diagnostic tools to assist healthcare professionals has increased significantly in recent years. However, AI is a heavily data-driven approach. Because of lack of data and especially ...

    • DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine 

      Thambawita, Vajira; Isaksen, Jonas L.; Hicks, Steven A.; Ghouse, Jonas; Ahlberg, Gustav; Linneberg, Allan; Grarup, Niels; Ellervik, Christina; Olesen, Morten Salling; Hansen, Torben; Graff, Claus; Holstein-Rathlou, Niels-Henrik; Strümke, Inga; Hammer, Hugo L.; Maleckar, Mary M.; Halvorsen, Pål; Riegler, Michael A.; Kanters, Jørgen K. (Scientific Reports;11, Article number: 21896 (2021), Peer reviewed; Journal article, 2021-11-09)
      Recent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem for collecting and sharing data between researchers. However, synthetic ...

    • An evaluation of using transformer networks for ECG Analysis 

      Yawar, Syeda Ambreen (Master thesis, 2023)
      Electrocardiogram (ECG) is a simulated recording of heart activity in electrical signals. It carries essential clinical information in the form of amplitude and timing. It is used to monitor and analyze the functionality ...

    • GANEx: A complete pipeline of training, inference and benchmarking GAN experiments 

      Thambawita, Vajira; Hammer, Hugo Lewi; Riegler, Michael Alexander; Halvorsen, Pål (International Workshop on Content-Based Multimedia Indexing, CBMI;, Conference object, 2019-10-21)
      Deep learning (DL) is one of the standard methods in the field of multimedia research to perform data classification, detection, segmentation and generation. Within DL, generative adversarial networks (GANs) represents a ...

    • Generating Synthetic Medical Images with 3D GANs 

      Guttulsrud, Håkon (Master thesis, 2023)
      This thesis presents a novel approach to overcoming the challenges associated with the scarcity of annotated medical image data, a significant hurdle in cancer detection. We propose the use of Generative Adversarial Networks ...

    • 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 ...

    • An Investigation into using Deep Convolutional Neural Networks for ECG Analysis 

      Hameed, Mohammad Awais (Master thesis, 2023)
      In this day and age, the fascination surrounding deep learning and AI is at its absolute peak. Both in terms of hype and controversy the current interest level is unprecedented, with exciting developments happening at a ...

    • 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 ...

    • 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 ...

    • 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 ...

    • ScopeSense: An 8.5-Month Sport, Nutrition, and Lifestyle Lifelogging Dataset 

      Riegler, Michael Alexander; Thambawita, Vajira; Nguyen, Thu; Hicks, Steven Alexander; Pettersen, Svein Arne; Telle-Hansen, Vibeke; Johansen, Dag; Jain, Ramesh; Halvorsen, Pål (Lecture Notes in Computer Science (LNCS);, Peer reviewed; Journal article, 2023)
      Nowadays, most people have a smartphone that can track their everyday activities. Furthermore, a significant number of people wear advanced smartwatches to track several vital biomarkers in addition to activity data. ...

    • Synthesizing a Talking Child Avatar to Train Interviewers Working with Maltreated Children 

      Salehi, Pegah; Hassan, Syed Zohaib; Lammerse, Myrthe; Shafiee Sabet, Saeed; Riiser, Ingvild; Røed, Ragnhild Klingenberg; Sinkerud Johnson, Miriam; Hicks, Steven; Thambawita, Vajira; Powell, Martine; Lamb, Michael E.; Baugerud, Gunn Astrid; Halvorsen, Pål; Riegler, Michael (Big Data and Cognitive Computing;Volume 6 / Issue 2, Peer reviewed; Journal article, 2022-06-01)
      When responding to allegations of child sexual, physical, and psychological abuse, Child Protection Service (CPS) workers and police personnel need to elicit detailed and accurate accounts of the abuse to assist in ...

    • Toadstool: a dataset for training emotional intelligent machines playing Super Mario Bros 

      Svoren, Henrik; Thambawita, Vajira; Halvorsen, Pål; Jakobsen, Petter; Garcia-Ceja, Enrique; Noori, Farzan Majeed; Hammer, Hugo Lewi; Lux, Mathias; Riegler, Michael; Hicks, Steven (MMSys: Multimedia Systems;MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference, Conference object, 2020)
      Games are often defined as engines of experience, and they are heavily relying on emotions, they arouse in players. In this paper, we present a dataset called Toadstool as well as a reproducible methodology to extend ...