An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning

Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole slide images (WSIs), which requires researchers to adopt patch-based methods and laborious free-hand contouring. Here, the authors develop a whole-slide training method to classify types of lung cancers...

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Main Authors:	Chi-Long Chen, Chi-Chung Chen, Wei-Hsiang Yu, Szu-Hua Chen, Yu-Chan Chang, Tai-I Hsu, Michael Hsiao, Chao-Yuan Yeh, Cheng-Yu Chen
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-02-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-021-21467-y

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spelling	doaj-47ff16da572b44e18dc48650a6b00d472021-02-21T12:11:56ZengNature Publishing GroupNature Communications2041-17232021-02-0112111310.1038/s41467-021-21467-yAn annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learningChi-Long Chen0Chi-Chung Chen1Wei-Hsiang Yu2Szu-Hua Chen3Yu-Chan Chang4Tai-I Hsu5Michael Hsiao6Chao-Yuan Yeh7Cheng-Yu Chen8Department of Pathology, School of Medicine, College of Medicine, Taipei Medical UniversityaetherAI Co., Ltd.aetherAI Co., Ltd.aetherAI Co., Ltd.Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming UniversityGenomics Research Center, Academia SinicaGenomics Research Center, Academia SinicaaetherAI Co., Ltd.Department of Radiology, School of Medicine, College of Medicine, Taipei Medical UniversityDeep learning for digital pathology is hindered by the extremely high spatial resolution of whole slide images (WSIs), which requires researchers to adopt patch-based methods and laborious free-hand contouring. Here, the authors develop a whole-slide training method to classify types of lung cancers using slide-level diagnoses with deep learning.https://doi.org/10.1038/s41467-021-21467-y
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Chi-Long Chen Chi-Chung Chen Wei-Hsiang Yu Szu-Hua Chen Yu-Chan Chang Tai-I Hsu Michael Hsiao Chao-Yuan Yeh Cheng-Yu Chen
spellingShingle	Chi-Long Chen Chi-Chung Chen Wei-Hsiang Yu Szu-Hua Chen Yu-Chan Chang Tai-I Hsu Michael Hsiao Chao-Yuan Yeh Cheng-Yu Chen An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning Nature Communications
author_facet	Chi-Long Chen Chi-Chung Chen Wei-Hsiang Yu Szu-Hua Chen Yu-Chan Chang Tai-I Hsu Michael Hsiao Chao-Yuan Yeh Cheng-Yu Chen
author_sort	Chi-Long Chen
title	An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning
title_short	An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning
title_full	An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning
title_fullStr	An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning
title_full_unstemmed	An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning
title_sort	annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2021-02-01
description	Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole slide images (WSIs), which requires researchers to adopt patch-based methods and laborious free-hand contouring. Here, the authors develop a whole-slide training method to classify types of lung cancers using slide-level diagnoses with deep learning.
url	https://doi.org/10.1038/s41467-021-21467-y
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An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning

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