Browsing Vitenskapelige publikasjoner (TN-IDE) by Author "Janssen, Emiel"

Now showing items 1-7 of 7

    • Automatic diagnostic tool for predicting cancer grade in bladder cancer patients using deep learning 

      Wetteland, Rune; Engan, Kjersti; Eftestøl, Trygve Christian; Kvikstad, Vebjørn; Janssen, Emiel; Tøssebro, Erlend; Lillesand, Melinda (Peer reviewed; Journal article, 2021-08)
      The most common type of bladder cancer is urothelial carcinoma, which is among the cancer types with the highest recurrence rate and lifetime treatment cost per patient. Diagnosed patients are stratified into risk groups, ...

    • Balancing privacy and progress in artificial intelligence : Anonymization in histopathology for biomedical research and education 

      Kanwal, Neel; Janssen, Emiel; Engan, Kjersti (Frontiers of Artificial Intelligence, Ethics and Multidisciplinary Applications;, Chapter, 2024)
      The advancement of biomedical research heavily relies on access to large amounts of medical data. In the case of histopathology, Whole Slide Images (WSI) and clinicopathological information are valuable for developing ...

    • Detection and localization of melanoma skin cancer in histopathological whole slide images 

      Kanwal, Neel; Amundsen, Roger; Hardardottir, Helga; Tomasetti, Luca; Undersrud, Erling Sandøy; Janssen, Emiel; Engan, Kjersti (European Signal Processing Conference;, Chapter; Conference object, 2023)
      If melanoma is diagnosed and treated in its early stages can increase the survival rate. A projected increase in skin cancer incidents and a shortage of dermatopathologists have emphasized the need for computational pathology ...

    • Invasive cancerous area detection in non-muscle invasive bladder cancer whole slide images 

      Fuster Navarro, Saul; Khoraminia, Farbod; Kiraz, Umay; Kanwal, Neel; Kvikstad, Vebjørn; Eftestøl, Trygve Christian; Zuiverloon, Tahlita C M; Janssen, Emiel; Engan, Kjersti (Chapter, 2022)
      Bladder cancer patients’ stratification into risk groups relies on grade, stage and clinical factors. For non-muscle invasive bladder cancer, T1 tumours that invade the subepithelial tissue are high-risk lesions with a ...

    • A Multiscale Approach for Whole-Slide Image Segmentation of five Tissue Classes in Urothelial Carcinoma Slides 

      Wetteland, Rune; Engan, Kjersti; Eftestøl, Trygve Christian; Janssen, Emiel; Kvikstad, Vebjørn (Peer reviewed; Journal article, 2020)
      In pathology labs worldwide, we see an increasing number of tissue samples that need to be assessed without the same increase in the number of pathologists. Computational pathology, where digital scans of histological ...

    • NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning 

      Fuster Navarro, Saul; Kiraz, Umay; Eftestøl, Trygve Christian; Janssen, Emiel; Engan, Kjersti (Peer reviewed; Journal article, 2024)
      The most prevalent form of bladder cancer is urothelial carcinoma, characterized by a high recurrence rate and substantial lifetime treatment costs for patients. Grading is a prime factor for patient risk stratification, ...

    • Semi-supervised tissue segmentation of histological images 

      Wetteland, Rune; Dalheim, Ove Nicolai; Kvikstad, Vebjørn; Janssen, Emiel; Engan, Kjersti (Peer reviewed; Journal article, 2020-09)
      Supervised learning of convolutional neural networks (CNN) used for image classification and segmentation has produced state-of-the art results, including in many medical image applications. In the medical field, making ...