BU-HIM: Bayesian Uncertainty-Guided Hard Instance Mining for Whole Slide Image Classification

Whole slide image (WSI) classification is a fundamental task in computational pathology, yet its large-scale and weakly annotated nature poses significant challenges for effective representation learning. Existing multiple instance learning (MIL) methods typically rely on deterministic attention mechanisms to identify informative instances, but instances with comparable attention may exhibit substantially different levels of epistemic uncertainty, leading to suboptimal instance selection. To address this issue, we propose BU-HIM, a plug-and- play Bayesian uncertainty-guided hard instance mining framework for weakly supervised WSI classification. Specifically, aBayesian variational attention mechanism is introduced to jointly characterize instance attention and epistemic uncertainty, where attention variance serves as an uncertainty-aware criterion for identifying informative hard instances. Furthermore, a teacher–student collaborative learning strategy is developed to adaptively optimize the model using the selected hard instances while preserving global contextual information from the entire slide. Extensive experiments on the CAMELYON16 and PANDA benchmark datasets demonstrate that BU-HIM consistently improves the performance of various MIL backbones and achieves superior uncertainty estimation compared with MC-Dropout-based approaches. These results validate BU-HIM as an effective, uncertainty-aware, and backbone-compatible training framework for weakly supervised WSI classification.