Binary semantic segmentation for detection of prostate adenocarcinoma using an ensemble with attention and residual U-Net architectures.

ABSTRACT: An accurate determination of the Gleason Score (GS) or Gleason Pattern (GP) is crucial in the diagnosis of prostate cancer (PCa) because it is one of the criterion used to guide treatment decisions for prognostic-risk groups. However, the manually designation of GP by a pathologist using a microscope is prone to error and subject to significant inter-observer variability. Deep learning has been used to automatically differentiate GP on digitized slides, aiding pathologists and reducing inter-observer variability, especially in the early GP of cancer. This article presents a binary semantic segmentation for the GP of prostate adenocarcinoma. The segmentation separates benign and malignant tissues, with the malignant class consisting of adenocarcinoma GP3 and GP4 tissues annotated from 50 un

SUBMITTER: Damkliang K 

PROVIDER: S-EPMC10773872 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature