Using ensembles and distillation to optimize the deployment of deep learning models for the classification of electronic cancer pathology reports.
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.
Author(s): 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
DOI: 10.1093/jamiaopen/ooac075