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

Ensemble model distillation is a simple tool to reduce model overconfidence in problems with extreme class imbalance and noisy datasets. These methods can facilitate the deployment of deep learning models in high-risk domains with low computational resources where minimizing inference time is required.