Blar i Fakultet for teknologi, kunst og design (TKD) på tidsskrift "PLOS ONE"

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    • CANDIDATE: A tool for generating anonymous participant-linking IDs in multi-session studies 

      Sandnes, Frode Eika (PLOS ONE;16 (12): e0260569, Peer reviewed; Journal article, 2021-12-15)
      Background: To ensure the privacy of participants is an ethical and legal obligation for researchers. Yet, achieving anonymity can be technically difficult. When observing participants over time one needs mechanisms to ...

    • Implication of β2-adrenergic receptor and miR-196a correlation in neurite outgrowth of LNCaP prostate cancer cells 

      Guerriero, Ilaria; Ramberg, Håkon Andre; Sagini, Krizia; Ramirez-Garrastacho, Manuel; Tasken, Kristin Austlid; Llorente, Alicia (PLOS ONE;16 (6): e0253828, Peer reviewed; Journal article, 2021-06-30)
      The β2-adrenergic receptor has been shown to be involved in neuroendocrine differentiation and to contribute to the development of aggressive prostate cancer. In this study we have investigated whether miR-196a plays a ...

    • 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 ...

    • Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations 

      Hicks, Steven; Storås, Andrea; Riegler, Michael; Midoglu, Cise; Hammou, Malek; Lange, Thomas de; Parasa, Sravanthi; Halvorsen, Pål; Strumke, Inga (Peer reviewed; Journal article, 2024)
      Deep learning has achieved immense success in computer vision and has the potential to help physicians analyze visual content for disease and other abnormalities. However, the current state of deep learning is very much a ...