Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology.

ABSTRACT: A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a clinically-oriented approach to uncertainty quantification for whole-slide images, estimating uncertainty using dropout and calculating thresholds on training data to establish cutoffs for low- and high-confidence predictions. We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without uncertainty, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-

SUBMITTER: Dolezal JM 

PROVIDER: S-EPMC9630455 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature