Deep learning generates synthetic cancer histology for explainability and education

Deep learning generates synthetic cancer histology for explainability and education

Abstract Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, b...

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Bibliographic Details
Main Authors: James M. Dolezal, Rachelle Wolk, Hanna M. Hieromnimon, Frederick M. Howard, Andrew Srisuwananukorn, Dmitry Karpeyev, Siddhi Ramesh, Sara Kochanny, Jung Woo Kwon, Meghana Agni, Richard C. Simon, Chandni Desai, Raghad Kherallah, Tung D. Nguyen, Jefree J. Schulte, Kimberly Cole, Galina Khramtsova, Marina Chiara Garassino, Aliya N. Husain, Huihua Li, Robert Grossman, Nicole A. Cipriani, Alexander T. Pearson
Format: Article
Language:English
Published: Nature Portfolio 2023-05-01
Series:npj Precision Oncology
Online Access:https://doi.org/10.1038/s41698-023-00399-4

Internet

https://doi.org/10.1038/s41698-023-00399-4

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