Interpretable survival prediction for colorectal cancer using deep learning

Interpretable survival prediction for colorectal cancer using deep learning

Abstract Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting disease-specific survival for stage II and III colorectal cancer using 3652 cases (27,300 slides)...

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Auteurs principaux: Ellery Wulczyn, David F. Steiner, Melissa Moran, Markus Plass, Robert Reihs, Fraser Tan, Isabelle Flament-Auvigne, Trissia Brown, Peter Regitnig, Po-Hsuan Cameron Chen, Narayan Hegde, Apaar Sadhwani, Robert MacDonald, Benny Ayalew, Greg S. Corrado, Lily H. Peng, Daniel Tse, Heimo Müller, Zhaoyang Xu, Yun Liu, Martin C. Stumpe, Kurt Zatloukal, Craig H. Mermel
Format: article
Langue:EN
Publié: Nature Portfolio 2021
Sujets:
Computer applications to medicine. Medical informatics
R858-859.7
Accès en ligne:https://doaj.org/article/da537cd907ad48cb854376055689af37
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https://doaj.org/article/da537cd907ad48cb854376055689af37

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