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, Alexander T. Pearson

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number6572
JournalNature communications
Volume13
Issue number1
DOIs
StatePublished - Dec 2022

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • General
  • Physics and Astronomy(all)

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