Article
Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes.
Nature communications - 12 Mar 2021
Diao James A, Wang Jason K, Chui Wan Fung, Mountain Victoria, Gullapally Sai Chowdary, Srinivasan Ramprakash, Mitchell Richard N, Glass Benjamin, Hoffman Sara, Rao Sudha K, Maheshwari Chirag, Lahiri Abhik, Prakash Aaditya, McLoughlin Ryan, Kerner Jennifer K, Resnick Murray B, Montalto Michael C, Khosla Aditya, Wapinski Ilan N, Beck Andrew H, Elliott Hunter L, Taylor-Weiner Amaro
Abstract excerpt
Computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still, lack of interpretability remains a significant barrier to clinical integration. We present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features...
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