Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images

Machine-assisted recognition of colorectal cancer has been mainly focused on supervised deep learning that suffers from a significant bottleneck of requiring massive amounts of labeled data. Here, the authors propose a semi-supervised model based on the mean teacher architecture that provides pathol...

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Autores principales:	Gang Yu, Kai Sun, Chao Xu, Xing-Hua Shi, Chong Wu, Ting Xie, Run-Qi Meng, Xiang-He Meng, Kuan-Song Wang, Hong-Mei Xiao, Hong-Wen Deng
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/69b6a2073a554bada6f7413199f05840
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id	oai:doaj.org-article:69b6a2073a554bada6f7413199f05840
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spelling	oai:doaj.org-article:69b6a2073a554bada6f7413199f058402021-11-08T11:05:43ZAccurate recognition of colorectal cancer with semi-supervised deep learning on pathological images10.1038/s41467-021-26643-82041-1723https://doaj.org/article/69b6a2073a554bada6f7413199f058402021-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-26643-8https://doaj.org/toc/2041-1723Machine-assisted recognition of colorectal cancer has been mainly focused on supervised deep learning that suffers from a significant bottleneck of requiring massive amounts of labeled data. Here, the authors propose a semi-supervised model based on the mean teacher architecture that provides pathological predictions at both patch- and patient-levels.Gang YuKai SunChao XuXing-Hua ShiChong WuTing XieRun-Qi MengXiang-He MengKuan-Song WangHong-Mei XiaoHong-Wen DengNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Gang Yu Kai Sun Chao Xu Xing-Hua Shi Chong Wu Ting Xie Run-Qi Meng Xiang-He Meng Kuan-Song Wang Hong-Mei Xiao Hong-Wen Deng Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images
description	Machine-assisted recognition of colorectal cancer has been mainly focused on supervised deep learning that suffers from a significant bottleneck of requiring massive amounts of labeled data. Here, the authors propose a semi-supervised model based on the mean teacher architecture that provides pathological predictions at both patch- and patient-levels.
format	article
author	Gang Yu Kai Sun Chao Xu Xing-Hua Shi Chong Wu Ting Xie Run-Qi Meng Xiang-He Meng Kuan-Song Wang Hong-Mei Xiao Hong-Wen Deng
author_facet	Gang Yu Kai Sun Chao Xu Xing-Hua Shi Chong Wu Ting Xie Run-Qi Meng Xiang-He Meng Kuan-Song Wang Hong-Mei Xiao Hong-Wen Deng
author_sort	Gang Yu
title	Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images
title_short	Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images
title_full	Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images
title_fullStr	Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images
title_full_unstemmed	Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images
title_sort	accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/69b6a2073a554bada6f7413199f05840
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Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images

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