SIB-MIL: Sparsity-Induced Bayesian Neural Network for Robust Multiple Instance Learning on Whole Slide Image Analysis

Yihang Chen, Tsai Hor Chan, Jianning Chen, Liang Li, Guosheng Yin, Lequan Yu

Abstract: Multiple instance learning (MIL) has shown prominent success in analyzing whole slide histopathology images (WSIs). However, existing MIL methods often suffer from overfitting due to weak supervision and the “needle-in-a-haystack” nature of WSIs. Additionally, most deterministic approaches lack a mechanism for uncertainty quantification. While Bayesian neural networks (BNNs) have emerged as a promising solution to mitigate overfitting and enable uncertainty estimation by imposing prior constraints, commonly used Gaussian BNNs exhibit unstable posterior predictive distributions under weak supervision and suffer from high prediction variance. To tackle these challenges, we propose a sparsity-induced Bayesian Neural Network to be adopted in the MIL scheme, named SIB-MIL, for robust WSI prediction. Instead of using Gaussian prior distributions, we place a sparsity-induced prior, the Horse-shoe prior, on the BNN parameters to address the variance overflowing issue. Such sparsity also filters unimportant noise and highlights salient regions, which only occupy a small proportion in WSIs. Empirical evaluations on cancer classification and subtyping tasks corroborate that not only can our method improve the existing MIL networks, but it also performs well in uncertainty quantification. Codes are available at https://github.com/HKU-MedAI/SIB-MIL.

External IDs:doi:10.1109/tmi.2025.3638243