A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer

A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer

The Gleason score is an important predictor of prognosis in prostate cancer. However, its subjective nature can result in over- or under-grading. Our objective was to train an artificial intelligence (AI)-based algorithm to grade prostate cancer in specimens from patients who underwent radical prost...

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Bibliographic Details
Main Authors: Sushant Patkar, Stephanie Harmon, Isabell Sesterhenn, Rosina Lis, Maria Merino, Denise Young, G. Thomas Brown, Kimberly M. Greenfield, John D. McGeeney, Sally Elsamanoudi, Shyh-Han Tan, Cara Schafer, Jiji Jiang, Gyorgy Petrovics, Albert Dobi, Francisco J. Rentas, Peter A. Pinto, Gregory T. Chesnut, Peter Choyke, Baris Turkbey, Joel T. Moncur
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Journal of Pathology Informatics
Subjects:
Prostate cancer
Digital pathology
Gleason grading
Artificial intelligence
Online Access:http://www.sciencedirect.com/science/article/pii/S2153353924000208
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