Improving the repeatability of deep learning models with Monte Carlo dropout.

Lemay, Andreanne; Hoebel, Katharina; Bridge, Christopher P; Befano, Brian; De Sanjosé, Silvia; Egemen, Didem; Rodriguez, Ana Cecilia; Schiffman, Mark; Campbell, John Peter; Kalpathy-Cramer, Jayashree

Lemay, Andreanne; Hoebel, Katharina; Bridge, Christopher P; Befano, Brian; De Sanjosé, Silvia; Egemen, Didem; Rodriguez, Ana Cecilia; Schiffman, Mark; Campbell, John Peter; Kalpathy-Cramer, Jayashree.

Afiliación

Lemay A; Martinos Center for Biomedical Imaging, Boston, MA, USA.
Hoebel K; NeuroPoly, Polytechnique Montreal, Montreal, QC, Canada.
Bridge CP; Martinos Center for Biomedical Imaging, Boston, MA, USA.
Befano B; Massachusetts Institute of Technology, Cambridge, MA, USA.
De Sanjosé S; Martinos Center for Biomedical Imaging, Boston, MA, USA.
Egemen D; MGH & BWH Center for Clinical Data Science, Boston, MA, USA.
Rodriguez AC; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA.
Schiffman M; Division of Cancer Epidemiology & Genetics, National Cancer Institute, Rockville, MD, USA.
Campbell JP; Division of Cancer Epidemiology & Genetics, National Cancer Institute, Rockville, MD, USA.
Kalpathy-Cramer J; Division of Cancer Epidemiology & Genetics, National Cancer Institute, Rockville, MD, USA.

NPJ Digit Med ; 5(1): 174, 2022 Nov 18.

Article en En | MEDLINE | ID: mdl-36400939

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: NPJ Digit Med Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

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