HEAL: an automated deep learning framework for cancer histopathology image analysis

Yanan Wang, Nicolas Coudray, YUN Zhao, Fuyi Li, Changyuan Hu, Yao-Zhong Zhang, Seiya Imoto, Aristotelis Tsirigos, Geoff Webb, Roger J. Daly, Jiangning Song

Research output: Contribution to journalArticleResearchpeer-review

18 Citations (Scopus)

Abstract

Motivation: Digital pathology supports analysis of histopathological images using deep learning methods at a large-scale. However, applications of deep learning in this area have been limited by the complexities of configuration of the computational environment and of hyperparameter optimization, which hinder deployment and reduce reproducibility. Results: Here, we propose HEAL, a deep learning-based automated framework for easy, flexible and multi-faceted histopathological image analysis. We demonstrate its utility and functionality by performing two case studies on lung cancer and one on colon cancer. Leveraging the capability of Docker, HEAL represents an ideal end-to-end tool to conduct complex histopathological analysis and enables deep learning in a broad range of applications for cancer image analysis.

Original languageEnglish
Pages (from-to)4291-4295
Number of pages5
JournalBioinformatics
Volume37
Issue number22
DOIs
Publication statusPublished - 15 Nov 2021

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