Dispersion-to-consolidation: Consolidating dispersed semantics via context-aware clustering for whole slide image...

Multiple Instance Learning (MIL) has become the dominant paradigm for analyzing histopathology whole slide images (WSIs). However, the inherent spatial heterogeneity of WSIs poses a significant challenge to existing MIL methods. This heterogeneity, where morphologically similar tissue patches often exhibit multifocal distributions across the WSI, hinders the ability of current models to effectively capture long-range spatial dependencies and complex inter-tissue semantic associations. To mitigate these limitations, we propose DisCo, a novel Dispersion-to-Consolidation MIL framework that consolidates dispersed semantics through context-aware clustering for WSI analysis. DisCo operates by iteratively routing and aggregating instances into a compact set of semantic anchors that represent distinct morphological patterns. Specifically, a Cluster Router module dynamically aggregates spatially dispersed yet semantically similar instances into tissue-specific semantic groups, strengthening intra-group feature interactions through dynamic routing and local aggregation. Subsequently, a Cluster Merger module consolidates redundant semantic anchors and captures inter-group associations by learning a dynamic assignment that projects fine-grained anchors into coarser anchors. Extensive experiments on 15 large-scale public cancer datasets across three challenging tasks demonstrate that DisCo consistently outperforms state-of-the-art methods. The source code is available at https://github.com/junjianli106/DisCo.