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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...
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. |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
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 |
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