DOMINO: Domain-aware loss for deep learning calibration

Deep learning has achieved the state-of-the-art performance across medical imaging tasks; however, model calibration is often not considered. Uncalibrated models are potentially dangerous in high-risk applications since the user does not know when they will fail. Therefore, this paper proposes a nov...

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Detalles Bibliográficos
Autores principales: Stolte, Skylar E., Volle, Kyle, Indahlastari, Aprinda, Albizu, Alejandro, Woods, Adam J., Brink, Kevin, Hale, Matthew, Fang, Ruogu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10118072/
https://www.ncbi.nlm.nih.gov/pubmed/37091721
http://dx.doi.org/10.1016/j.simpa.2023.100478

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10118072/
https://www.ncbi.nlm.nih.gov/pubmed/37091721
http://dx.doi.org/10.1016/j.simpa.2023.100478

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