Machine learning in computational histopathology: Challenges and opportunities.

Autor: Cooper M; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.; University Health Network, Toronto, Ontario, Canada.; Vector Institute, Toronto, Ontario, Canada., Ji Z; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.; Vector Institute, Toronto, Ontario, Canada., Krishnan RG; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.; Vector Institute, Toronto, Ontario, Canada.; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
Jazyk: angličtina
Zdroj: Genes, chromosomes & cancer [Genes Chromosomes Cancer] 2023 Sep; Vol. 62 (9), pp. 540-556. Date of Electronic Publication: 2023 Jun 14.
DOI: 10.1002/gcc.23177
Abstrakt: Digital histopathological images, high-resolution images of stained tissue samples, are a vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state based on these images are an important part of oncology workflow. Although pathology workflows have historically been conducted in laboratories under a microscope, the increasing digitization of histopathological images has led to their analysis on computers in the clinic. The last decade has seen the emergence of machine learning, and deep learning in particular, a powerful set of tools for the analysis of histopathological images. Machine learning models trained on large datasets of digitized histopathology slides have resulted in automated models for prediction and stratification of patient risk. In this review, we provide context for the rise of such models in computational histopathology, highlight the clinical tasks they have found success in automating, discuss the various machine learning techniques that have been applied to this domain, and underscore open problems and opportunities.
(© 2023 The Authors. Genes, Chromosomes and Cancer published by Wiley Periodicals LLC.)
Databáze: MEDLINE
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