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

TL;DR

This study introduces a uncertainty quantification method for digital histopathology models, improving prediction confidence and robustness across diverse datasets and domain shifts in cancer diagnosis.

Contribution

The paper presents a novel uncertainty quantification approach using dropout and thresholding, enhancing high-confidence predictions in digital histopathology.

Findings

High-confidence predictions outperform standard models.

UQ remains reliable under domain shift.

Method effective on large external datasets.

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.