Deep learning generates synthetic cancer histology for explainability and education

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, but explai...

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Detalles Bibliográficos
Autores principales: Dolezal, James M., Wolk, Rachelle, Hieromnimon, Hanna M., Howard, Frederick M., Srisuwananukorn, Andrew, Karpeyev, Dmitry, Ramesh, Siddhi, Kochanny, Sara, Kwon, Jung Woo, Agni, Meghana, Simon, Richard C., Desai, Chandni, Kherallah, Raghad, Nguyen, Tung D., Schulte, Jefree J., Cole, Kimberly, Khramtsova, Galina, Garassino, Marina Chiara, Husain, Aliya N., Li, Huihua, Grossman, Robert, Cipriani, Nicole A., Pearson, Alexander T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227067/
https://www.ncbi.nlm.nih.gov/pubmed/37248379
http://dx.doi.org/10.1038/s41698-023-00399-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227067/
https://www.ncbi.nlm.nih.gov/pubmed/37248379
http://dx.doi.org/10.1038/s41698-023-00399-4

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