Deep learning powers cancer diagnosis in digital pathology

Yunjie He; Hong Zhao; Stephen T.C. Wong

doi:10.1016/j.compmedimag.2020.101820

Deep learning powers cancer diagnosis in digital pathology

Yunjie He, Hong Zhao, Stephen T.C. Wong

Research output: Contribution to journal › Review article › peer-review

5 Scopus citations

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.

Original language	English (US)
Article number	101820
Journal	Computerized Medical Imaging and Graphics
Volume	88
DOIs	https://doi.org/10.1016/j.compmedimag.2020.101820
State	Published - Mar 2021

Keywords

AI
Digital pathology
cancer diagnosis
deep learning
graph neural networks
microscopy image

ASJC Scopus subject areas

Radiological and Ultrasound Technology
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Health Informatics
Computer Graphics and Computer-Aided Design

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10.1016/j.compmedimag.2020.101820

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@article{55803cadbbb4488fb44242b0eec477df,

title = "Deep learning powers cancer diagnosis in digital pathology",

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.",

keywords = "AI, Digital pathology, cancer diagnosis, deep learning, graph neural networks, microscopy image",

author = "Yunjie He and Hong Zhao and Wong, {Stephen T.C.}",

note = "Funding Information: This work was funded by NIHR01CA238727 to H.Z and S.T.C.W, and NIH U01CA253553, NIH R01CA251710, John S. Dunn Research Foundation, and T.T. and W.F. Chao Foundation to S.T.C.W. Funding Information: This work was funded by NIH R01CA238727 to H.Z and S.T.C.W, and NIH U01CA253553 , NIH R01CA251710 , John S. Dunn Research Foundation , and T.T. and W.F. Chao Foundation to S.T.C.W. Publisher Copyright: {\textcopyright} 2020 Elsevier Ltd",

year = "2021",

month = mar,

doi = "10.1016/j.compmedimag.2020.101820",

language = "English (US)",

volume = "88",

journal = "Computerized Medical Imaging and Graphics",

issn = "0895-6111",

publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Deep learning powers cancer diagnosis in digital pathology

AU - He, Yunjie

AU - Zhao, Hong

AU - Wong, Stephen T.C.

N1 - Funding Information: This work was funded by NIHR01CA238727 to H.Z and S.T.C.W, and NIH U01CA253553, NIH R01CA251710, John S. Dunn Research Foundation, and T.T. and W.F. Chao Foundation to S.T.C.W. Funding Information: This work was funded by NIH R01CA238727 to H.Z and S.T.C.W, and NIH U01CA253553 , NIH R01CA251710 , John S. Dunn Research Foundation , and T.T. and W.F. Chao Foundation to S.T.C.W. Publisher Copyright: © 2020 Elsevier Ltd

PY - 2021/3

Y1 - 2021/3

N2 - 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.

AB - 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.

KW - AI

KW - Digital pathology

KW - cancer diagnosis

KW - deep learning

KW - graph neural networks

KW - microscopy image

UR - http://www.scopus.com/inward/record.url?scp=85099292305&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85099292305&partnerID=8YFLogxK

U2 - 10.1016/j.compmedimag.2020.101820

DO - 10.1016/j.compmedimag.2020.101820

M3 - Review article

C2 - 33453648

AN - SCOPUS:85099292305

VL - 88

JO - Computerized Medical Imaging and Graphics

JF - Computerized Medical Imaging and Graphics

SN - 0895-6111

M1 - 101820

ER -

Deep learning powers cancer diagnosis in digital pathology

Abstract

Keywords

ASJC Scopus subject areas

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