Uncertainty-based Data-wise Label Smoothing for Calibrating Multiple Instance Learning in Histopathology Image Classification

Hyeongmin Park, Sungrae Hong, Chanjae Song, Jongwoo Kim, Mun Yong Yi

Abstract:

Deep neural networks (DNNs) have transformed
biomedical image analysis, particularly in histopathology
with Whole Slide Images (WSIs) classification. However,
training DNNs requires large annotated datasets, which is
challenging due to the high heterogeneity and high resolution of WSIs. Multiple Instance Learning (MIL) has become a popular method for weakly supervised classification
in this context, training with only slide-level labels. Despite the advancements, ensuring the reliability of model
performance is crucial in safety-critical domains including
healthcare. Deep learning models in real-world decisionmaking systems must accurately predict probability estimates to reflect the true likelihood of correctness, known
as confidence calibration. This study introduces a novel
calibration framework, UDLS, which uses data-wise label
smoothing based on predictive uncertainty to improve the
calibration of MIL frameworks. This approach involves
augmenting WSIs with PatchFeatureDropout, computing
predictive uncertainty estimates for original data, and applying these estimates to each sample for label smoothing
during model training. Experimental results on benchmark
histopathology datasets show noticeable improvements in
both calibration and classification performance, highlighting UDLS’s potential for enhancing the reliability of predictions from deep learning models in clinical settings.