Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology

A model’s ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a clinically-oriented approach to unce...

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Detalles Bibliográficos
Autores principales: Dolezal, James M., Srisuwananukorn, Andrew, Karpeyev, Dmitry, Ramesh, Siddhi, Kochanny, Sara, Cody, Brittany, Mansfield, Aaron S., Rakshit, Sagar, Bansal, Radhika, Bois, Melanie C., Bungum, Aaron O., Schulte, Jefree J., Vokes, Everett E., Garassino, Marina Chiara, Husain, Aliya N., Pearson, Alexander T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9630455/
https://www.ncbi.nlm.nih.gov/pubmed/36323656
http://dx.doi.org/10.1038/s41467-022-34025-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9630455/
https://www.ncbi.nlm.nih.gov/pubmed/36323656
http://dx.doi.org/10.1038/s41467-022-34025-x

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