Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology

• URL: http://arxiv.org/abs/2204.04516v1
• Date: Sat, 9 Apr 2022 17:35:37 GMT
• Title: Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology
• Authors: James M Dolezal, Andrew Srisuwananukorn, Dmitry Karpeyev, Siddhi Ramesh, Sara Kochanny, Brittany Cody, Aaron Mansfield, Sagar Rakshit, Radhika Bansa, Melanie Bois, Aaron O Bungum, Jefree J Schulte, Everett E Vokes, Marina Chiara Garassino, Aliya N Husain, Alexander T Pearson
• Abstract summary: We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without UQ. 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.
• Score: 40.96261204117952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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