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

Machine-assisted pathological recognition has been focused on supervised learning (SL) that suffers from a significant annotation bottleneck. We propose a semi-supervised learning (SSL) method based on the mean teacher architecture using 13,111 whole slide images of colorectal cancer from 8803 subje...

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Detalles Bibliográficos
Autores principales: Yu, Gang, Sun, Kai, Xu, Chao, Shi, Xing-Hua, Wu, Chong, Xie, Ting, Meng, Run-Qi, Meng, Xiang-He, Wang, Kuan-Song, Xiao, Hong-Mei, Deng, Hong-Wen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563931/
https://www.ncbi.nlm.nih.gov/pubmed/34728629
http://dx.doi.org/10.1038/s41467-021-26643-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563931/
https://www.ncbi.nlm.nih.gov/pubmed/34728629
http://dx.doi.org/10.1038/s41467-021-26643-8

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