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

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. De-spite the advancements, ensuring the reliability of model performance is crucial in safety-critical domains including healthcare. Deep learning models in real-world decision-making 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 pre-dictions from deep learning models in clinical settings.