Nuclei instance segmentation from histopathology images using Bayesian dropout based deep learning.

Journal: BMC medical imaging
Published Date: Oct 19, 2023

Abstract

BACKGROUND: The deterministic deep learning models have achieved state-of-the-art performance in various medical image analysis tasks, including nuclei segmentation from histopathology images. The deterministic models focus on improving the model prediction accuracy without assessing the confidence in the predictions.

Authors

  • Naga Raju Gudhe
    Institute of Clinical Medicine, Pathology and Forensic Medicine, Multidisciplinary Cancer research community RC Cancer, University of Eastern Finland, P.O. Box 1627, Kuopio, 70211, Finland. raju.gudhe@uef.fi.
  • Veli-Matti Kosma
    Institute of Clinical Medicine, Pathology and Forensic Medicine, and Translational Cancer Research Area, University of Eastern Finland, P.O. Box 1627, FI-70211, Kuopio, Finland.
  • Hamid Behravan
    Institute of Clinical Medicine, Pathology and Forensic Medicine, and Translational Cancer Research Area, University of Eastern Finland, P.O. Box 1627, FI-70211, Kuopio, Finland. hamidbeh@uef.fi.
  • Arto Mannermaa
    Institute of Clinical Medicine, Pathology and Forensic Medicine, and Translational Cancer Research Area, University of Eastern Finland, P.O. Box 1627, FI-70211, Kuopio, Finland.

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