Nuclear shape, architecture and orientation features from H&E images are able to predict recurrence in node-negative gastric adenocarcinoma-Reference-Cited by-同舟云学术

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Nuclear shape, architecture and orientation features from H&E images are able to predict recurrence in node-negative gastric adenocarcinoma

Published:2019-03-18 Issue:1 Volume:17 Page:
ISSN:1479-5876
Container-title:Journal of Translational Medicine
language:en
Short-container-title:J Transl Med

Author:

Ji Meng-Yao,Yuan Lei,Jiang Xiao-Da,Zeng Zhi,Zhan Na,Huang Ping-Xiao,Lu Cheng,Dong Wei-Guo

Funder

Natural Science Foundation of Hubei Province

National Natural Science Foundation of China

Natural Science Basic Research Plan in Shaanxi Province of China

Innovation Seed Funding of Wuhan University

Fundamental Research Funds for the Central Universities

Publisher

Springer Science and Business Media LLC

Subject

General Biochemistry, Genetics and Molecular Biology,General Medicine

Link

http://link.springer.com/content/pdf/10.1186/s12967-019-1839-x.pdf

Reference28 articles.

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3. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Corrigendum: dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;546:686.

4. Madabhushi A, Agner S, Basavanhally A, Doyle S, Lee G. Computer-aided prognosis: predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data. Comput Med Imaging Graph. 2011;35:506–14.

5. Yu KH, Zhang C, Berry GJ, Altman RB, Re C, Rubin DL, Snyder M. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun. 2016;7:12474.

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