Hard example mining in Multi-Instance Learning for Whole-Slide Image Classification

Multiple instance learning(MIL) has shown superior performance in the classification of whole-slide images(WSIs). The implementation of multiple instance learning for WSI classification typically involves two components, i.e., a feature extractor, which is used to extract features from patches, and an MIL aggregator, responsible for generating WSI features from the patch features, also contributing to the final classification of WSIs. MIL aggregators often employs a specific MIL classification module. To ensure interactive optimization of the feature extractor and the MIL aggregator, existing state-of-the-art methods select patches based on attention scores to optimize the feature extractor. However, they predominantly focus on easy-to-classify instances, leading to inadequate capabilities in discriminating hard-toclassify instances. In this paper, we introduces a novel Multiple Instance Learning method, HPA-MIL (Hard Pseudo-label Assignment), which directly mines hard instances through pseudo-label assignment. Our experiments demonstrate that HPA-MIL achieves an AUC of 0.9523 on the TCGA NSCLC dataset, which outperforms all the existing state-of-the-art methods compared.