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

James M. Dolezal, Andrew Srisuwananukorn, Dmitry Karpeyev, Siddhi Ramesh, Sara Kochanny, Brittany Cody, Aaron S. Mansfield, Sagar Rakshit, Radhika Bansal, Melanie C. Bois, Aaron O. Bungum, Jefree J. Schulte, Everett E. Vokes, Marina Chiara Garassino, Aliya N. Husain and Alexander T. Pearson ()
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James M. Dolezal: University of Chicago Medical Center
Andrew Srisuwananukorn: Icahn School of Medicine at Mount Sinai
Dmitry Karpeyev: DV Group, LLC
Siddhi Ramesh: University of Chicago Medical Center
Sara Kochanny: University of Chicago Medical Center
Brittany Cody: University of Chicago
Aaron S. Mansfield: Mayo Clinic
Sagar Rakshit: Mayo Clinic
Radhika Bansal: Mayo Clinic
Melanie C. Bois: Mayo Clinic
Aaron O. Bungum: Mayo Clinic
Jefree J. Schulte: University of Wisconsin at Madison
Everett E. Vokes: University of Chicago Medical Center
Marina Chiara Garassino: University of Chicago Medical Center
Aliya N. Husain: University of Chicago
Alexander T. Pearson: University of Chicago Medical Center

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: 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-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that uncertainty 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.

Date: 2022
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DOI: 10.1038/s41467-022-34025-x

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