Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology
This work addresses the need for trustworthy computational biomarkers in clinical settings, particularly for cancer diagnosis, though it is incremental as it builds on existing uncertainty quantification techniques.
The authors tackled the problem of predictive uncertainty in digital histopathology by developing a novel uncertainty quantification method using dropout and thresholding, which improved high-confidence predictions for lung cancer classification on external datasets and maintained reliability under domain shift.
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.