Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer
Colonoscopy is the most commonly used tool to screen for colorectal cancer (CRC). Here, the authors develop a deep learning model to perform optical diagnosis of CRC by training on a large data set of white-light colonoscopy images and achieve endoscopist-level performance on three independent datas...
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Nature Portfolio
2020
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oai:doaj.org-article:5c46ece027b64112af1315b6221f8c2b2021-12-02T17:52:29ZDiagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer10.1038/s41467-020-16777-62041-1723https://doaj.org/article/5c46ece027b64112af1315b6221f8c2b2020-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-16777-6https://doaj.org/toc/2041-1723Colonoscopy is the most commonly used tool to screen for colorectal cancer (CRC). Here, the authors develop a deep learning model to perform optical diagnosis of CRC by training on a large data set of white-light colonoscopy images and achieve endoscopist-level performance on three independent datasets.Dejun ZhouFei TianXiangdong TianLin SunXianghui HuangFeng ZhaoNan ZhouZuoyu ChenQiang ZhangMeng YangYichen YangXuexi GuoZhibin LiJia LiuJiefu WangJunfeng WangBangmao WangGuoliang ZhangBaocun SunWei ZhangDalu KongKexin ChenXiangchun LiNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-9 (2020) |
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DOAJ |
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DOAJ |
| language |
EN |
| topic |
Science Q |
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Science Q Dejun Zhou Fei Tian Xiangdong Tian Lin Sun Xianghui Huang Feng Zhao Nan Zhou Zuoyu Chen Qiang Zhang Meng Yang Yichen Yang Xuexi Guo Zhibin Li Jia Liu Jiefu Wang Junfeng Wang Bangmao Wang Guoliang Zhang Baocun Sun Wei Zhang Dalu Kong Kexin Chen Xiangchun Li Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer |
| description |
Colonoscopy is the most commonly used tool to screen for colorectal cancer (CRC). Here, the authors develop a deep learning model to perform optical diagnosis of CRC by training on a large data set of white-light colonoscopy images and achieve endoscopist-level performance on three independent datasets. |
| format |
article |
| author |
Dejun Zhou Fei Tian Xiangdong Tian Lin Sun Xianghui Huang Feng Zhao Nan Zhou Zuoyu Chen Qiang Zhang Meng Yang Yichen Yang Xuexi Guo Zhibin Li Jia Liu Jiefu Wang Junfeng Wang Bangmao Wang Guoliang Zhang Baocun Sun Wei Zhang Dalu Kong Kexin Chen Xiangchun Li |
| author_facet |
Dejun Zhou Fei Tian Xiangdong Tian Lin Sun Xianghui Huang Feng Zhao Nan Zhou Zuoyu Chen Qiang Zhang Meng Yang Yichen Yang Xuexi Guo Zhibin Li Jia Liu Jiefu Wang Junfeng Wang Bangmao Wang Guoliang Zhang Baocun Sun Wei Zhang Dalu Kong Kexin Chen Xiangchun Li |
| author_sort |
Dejun Zhou |
| title |
Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer |
| title_short |
Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer |
| title_full |
Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer |
| title_fullStr |
Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer |
| title_full_unstemmed |
Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer |
| title_sort |
diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer |
| publisher |
Nature Portfolio |
| publishDate |
2020 |
| url |
https://doaj.org/article/5c46ece027b64112af1315b6221f8c2b |
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