Deep Learning Powers Cancer Diagnosis in Digital Pathology

Overview

Journal Comput Med Imaging Graph

Specialty Radiology

Date 2021 Jan 16

PMID 33453648

Citations 10

Authors

Yunjie He

Hong Zhao

Stephen T C Wong

Affiliations

Soon will be listed here.

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.

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