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 novel, clinically-oriented approach to uncertainty quantification (UQ) 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 UQ, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that UQ thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts.
@article{arxiv.2204.04516,
title = {Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology},
author = {James M Dolezal and Andrew Srisuwananukorn and Dmitry Karpeyev and Siddhi Ramesh and Sara Kochanny and Brittany Cody and Aaron Mansfield and Sagar Rakshit and Radhika Bansa and Melanie Bois and Aaron O Bungum and Jefree J Schulte and Everett E Vokes and Marina Chiara Garassino and Aliya N Husain and Alexander T Pearson},
journal= {arXiv preprint arXiv:2204.04516},
year = {2023}
}