Using ensembles and distillation to optimize the deployment of deep learning models for the classification of electronic cancer pathology reports

OBJECTIVE: We aim to reduce overfitting and model overconfidence by distilling the knowledge of an ensemble of deep learning models into a single model for the classification of cancer pathology reports. MATERIALS AND METHODS: We consider the text classification problem that involves 5 individual ta...

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Detalles Bibliográficos
Autores principales: De Angeli, Kevin, Gao, Shang, Blanchard, Andrew, Durbin, Eric B, Wu, Xiao-Cheng, Stroup, Antoinette, Doherty, Jennifer, Schwartz, Stephen M, Wiggins, Charles, Coyle, Linda, Penberthy, Lynne, Tourassi, Georgia, Yoon, Hong-Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Research and Applications
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469924/
https://www.ncbi.nlm.nih.gov/pubmed/36110150
http://dx.doi.org/10.1093/jamiaopen/ooac075

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469924/
https://www.ncbi.nlm.nih.gov/pubmed/36110150
http://dx.doi.org/10.1093/jamiaopen/ooac075

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