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). Most studies have employed patch-based methods, which often require detailed annotation of image patches. This typically involves laborious free-hand contouring on WSIs. To alleviat...

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Detalles Bibliográficos
Autores principales: Chen, Chi-Long, Chen, Chi-Chung, Yu, Wei-Hsiang, Chen, Szu-Hua, Chang, Yu-Chan, Hsu, Tai-I, Hsiao, Michael, Yeh, Chao-Yuan, Chen, Cheng-Yu
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/PMC7896045/
https://www.ncbi.nlm.nih.gov/pubmed/33608558
http://dx.doi.org/10.1038/s41467-021-21467-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7896045/
https://www.ncbi.nlm.nih.gov/pubmed/33608558
http://dx.doi.org/10.1038/s41467-021-21467-y

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