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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...
| Autores principales: | , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2021
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| Materias: | |
| 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 |