Deep Learning-Based Gleason Grading of Prostate Cancer From Histopathology Images-Role of Multiscale Decision Aggregation and Data Augmentation

• Published in: IEEE journal of biomedical and health informatics Vol. 24; no. 5; pp. 1413 - 1426
• Main Authors: Karimi, Davood, Nir, Guy, Fazli, Ladan, Black, Peter C., Goldenberg, Larry, Salcudean, Septimiu E.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Biomedical imaging; Biopsy; Classification; Data augmentation; Deep Learning; Glands; Gleason grading; Histological Techniques; Histopathology; Humans; Image Interpretation, Computer-Assisted - methods; Informatics; Inspection; Learning algorithms; Machine learning; Male; Methods; Neoplasm Grading - methods; Neural networks; Observational learning; Patches (structures); Principal component analysis; Prostate - pathology; Prostate cancer; Prostatic Neoplasms - diagnosis; Prostatic Neoplasms - pathology; Quality; Regression models; Training; Training data

Visual inspection of histopathology images of stained biopsy tissue by expert pathologists is the standard method for grading of prostate cancer (PCa). However, this process is time-consuming and subject to high inter-observer variability. Machine learning-based methods have the potential to improve...