Wang Yanan, Coudray Nicolas, Zhao Yun, Li Fuyi, Hu Changyuan, Zhang Yao-Zhong, Imoto Seiya, Tsirigos Aristotelis, Webb Geoffrey I, Daly Roger J, Song Jiangning
Cancer Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.
Department of Cell Biology, Skirball Institute of Biomolecular Medicine, New York University School of Medicine, New York, NY 10016, USA.
Bioinformatics. 2021 Nov 18;37(22):4291-4295. doi: 10.1093/bioinformatics/btab380.
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.
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.
The docker image of HEAL is available at https://hub.docker.com/r/docurdt/heal and related documentation and datasets are available at http://heal.erc.monash.edu.au.
Supplementary data are available at Bioinformatics online.
数字病理学支持使用深度学习方法大规模分析组织病理学图像。然而，深度学习在该领域的应用受到计算环境配置和超参数优化复杂性的限制，这阻碍了其部署并降低了可重复性。
在此，我们提出了HEAL，这是一个基于深度学习的自动化框架，用于轻松、灵活且多方面的组织病理学图像分析。我们通过对肺癌进行两个案例研究以及对结肠癌进行一个案例研究来展示其效用和功能。借助Docker的功能，HEAL是进行复杂组织病理学分析的理想端到端工具，并能在广泛的癌症图像分析应用中实现深度学习。
HEAL的Docker镜像可在https://hub.docker.com/r/docurdt/heal获取，相关文档和数据集可在http://heal.erc.monash.edu.au获取。
补充数据可在《生物信息学》在线版获取。
