Blar i Publikasjoner fra Cristin på forfatter "Parasa, Sravanthi"

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    • Impact of image resolution on deep learning performance in endoscopy image classification: An experimental study using a large dataset of endoscopic images 

      Thambawita, Vajira L B; Strumke, Inga; Hicks, Steven; Halvorsen, Pål; Parasa, Sravanthi; Riegler, Michael (Diagnostics;Volume 11 / Issue 12, Peer reviewed; Journal article, 2021-11-24)
      Recent trials have evaluated the efficacy of deep convolutional neural network (CNN)- based AI systems to improve lesion detection and characterization in endoscopy. Impressive results are achieved, but many medical studies ...

    • 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 ...

    • 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 ...

    • 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 ...