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Deep learning powers cancer diagnosis in digital pathology.

Authors
  • He, Yunjie1
  • Zhao, Hong2
  • Wong, Stephen T C3
  • 1 Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center, Houston, TX, 77030, USA.
  • 2 Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center, Houston, TX, 77030, USA. Electronic address: [email protected]
  • 3 Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center, Houston, TX, 77030, USA. Electronic address: [email protected]
Type
Published Article
Journal
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Publication Date
Dec 11, 2020
Volume
88
Pages
101820–101820
Identifiers
DOI: 10.1016/j.compmedimag.2020.101820
PMID: 33453648
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Technological innovation has accelerated the pathological diagnostic process for cancer, especially in digitizing histopathology slides and incorporating deep learning-based approaches to mine the subvisual morphometric phenotypes for improving pathology diagnosis. In this perspective paper, we provide an overview on major deep learning approaches for digital pathology and discuss challenges and opportunities of such approaches to aid cancer diagnosis in digital pathology. In particular, the emerging graph neural network may further improve the performance and interpretability of deep learning in digital pathology. Copyright © 2020 Elsevier Ltd. All rights reserved.

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