MoEMambaMIL: Structure-Aware Selective State Space Modeling for Whole-Slide Image Analysis
Whole-slide image (WSI) analysis is challenging due to the gigapixel scale of slides and their inherent hierarchical multi-resolution structure. Existing multiple instance learning (MIL) approaches often model WSIs as unordered collections of patches, which limits their ability to capture structured dependencies between global tissue organization and local cellular patterns. Although recent State Space Models (SSMs) enable efficient modeling of long sequences, how to structure WSI tokens to fully exploit their spatial hierarchy remains an open problem.We propose MoEMambaMIL, a structure-aware SSM framework for WSI analysis that integrates region-nested selective scanning with mixture-of-e...
| Authors | Dongqing Xie, Yonghuang Wu |
|---|---|
| Primary category | cs.CV |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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