Predictive uncertainty estimation in deep learning for lung carcinoma classification in digital pathology under real dataset shifts

TL;DR

This study evaluates the robustness of deep learning models for lung carcinoma classification in digital pathology by assessing predictive uncertainty estimation methods under various real-world dataset shifts, emphasizing their importance for clinical reliability.

Contribution

It provides the first large-scale benchmark comparing uncertainty estimation methods under diverse dataset shifts in lung carcinoma classification.

Findings

Monte Carlo dropout, deep ensemble, and few-shot learning improve robustness.

Uncertainty estimation enhances model calibration under distribution shifts.

Different methods vary in effectiveness depending on shift type.

Abstract

Deep learning has shown tremendous progress in a wide range of digital pathology and medical image classification tasks. Its integration into safe clinical decision-making support requires robust and reliable models. However, real-world data comes with diversities that often lie outside the intended source distribution. Moreover, when test samples are dramatically different, clinical decision-making is greatly affected. Quantifying predictive uncertainty in models is crucial for well-calibrated predictions and determining when (or not) to trust a model. Unfortunately, many works have overlooked the importance of predictive uncertainty estimation. This paper evaluates whether predictive uncertainty estimation adds robustness to deep learning-based diagnostic decision-making systems. We investigate the effect of various carcinoma distribution shift scenarios on predictive performance and calibration. We first systematically investigate three popular methods for improving predictive uncertainty: Monte Carlo dropout, deep ensemble, and few-shot learning on lung adenocarcinoma classification as a primary disease in whole slide images. Secondly, we compare the effectiveness of the methods in terms of performance and calibration under clinically relevant distribution shifts such as in-distribution shifts comprising primary disease sub-types and other characterization analysis data; out-of-distribution shifts comprising well-differentiated cases, different organ origin, and imaging modality shifts. While studies on uncertainty estimation exist, to our best knowledge, no rigorous large-scale benchmark compares predictive uncertainty estimation including these dataset shifts for lung carcinoma classification.