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Multiple Instance Learning (MIL) and transformers are increasingly popular inhistopathology Whole Slide Image (WSI) classification. However, unlike humanpathologists who selectively observe specific regions of histopathology tissuesunder different magnifications, most methods do not incorporate multipleresolutions of the WSIs, hierarchically and attentively, thereby leading to aloss of focus on the WSIs and information from other resolutions. To resolvethis issue, we propose a Hierarchical Attention-Guided Multiple InstanceLearning framework to fully exploit the WSIs. This framework can dynamicallyand attentively discover the discriminative regions across multiple resolutionsof the WSIs. Within this framework, an Integrated Attention Transformer isproposed to further enhance the performance of the transformer and obtain amore holistic WSI (bag) representation. This transformer consists of multipleIntegrated Attention Modules, which is the combination of a transformer layerand an aggregation module that produces a bag representation based on everyinstance representation in that bag. The experimental results show that ourmethod achieved state-of-the-art performances on multiple datasets, includingCamelyon16, TCGA-RCC, TCGA-NSCLC, and an in-house IMGC dataset. The code isavailable at https://github.com/BearCleverProud/HAG-MIL.

Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classification