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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Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/47ff16da572b44e18dc48650a6b00d47
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spelling	oai:doaj.org-article:47ff16da572b44e18dc48650a6b00d472021-12-02T14:03:50ZAn annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning10.1038/s41467-021-21467-y2041-1723https://doaj.org/article/47ff16da572b44e18dc48650a6b00d472021-02-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-21467-yhttps://doaj.org/toc/2041-1723Deep 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.Chi-Long ChenChi-Chung ChenWei-Hsiang YuSzu-Hua ChenYu-Chan ChangTai-I HsuMichael HsiaoChao-Yuan YehCheng-Yu ChenNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q 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
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.
format	article
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
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 Portfolio
publishDate	2021
url	https://doaj.org/article/47ff16da572b44e18dc48650a6b00d47
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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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