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An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning
2021
Nature Communications
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 alleviate the burden of such contouring and obtain benefits from scaling up training with numerous WSIs, we develop a method for training neural networks on entire WSIs using only slide-level diagnoses. Our
doi:10.1038/s41467-021-21467-y
pmid:33608558
fatcat:thkwoy5xvbcc5jcnv6hd5nohv4