Hypergraph Mamba for Efficient Whole Slide Image Understanding

• URL: http://arxiv.org/abs/2505.17457v2
• Date: Mon, 04 Aug 2025 08:35:27 GMT
• Title: Hypergraph Mamba for Efficient Whole Slide Image Understanding
• Authors: Jiaxuan Lu, Yuhui Lin, Junyan Shi, Fang Yan, Dongzhan Zhou, Yue Gao, Xiaosong Wang,
• Abstract summary: Whole Slide Images (WSIs) in histo pose a significant challenge for medical image analysis due to their ultra-high resolution, massive scale, and intricate spatial relationships.<n>We introduce the WSI-HGMamba, a novel framework that unifies the high-order relational modeling capabilities of the Hypergraph Neural Networks (HGNNs) with the linear-time sequential modeling efficiency of the State Space Models.
• Score: 10.285000840656808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Whole Slide Images (WSIs) in histopathology pose a significant challenge for extensive medical image analysis due to their ultra-high resolution, massive scale, and intricate spatial relationships. Although existing Multiple Instance Learning (MIL) approaches like Graph Neural Networks (GNNs) and Transformers demonstrate strong instance-level modeling capabilities, they encounter constraints regarding scalability and computational expenses. To overcome these limitations, we introduce the WSI-HGMamba, a novel framework that unifies the high-order relational modeling capabilities of the Hypergraph Neural Networks (HGNNs) with the linear-time sequential modeling efficiency of the State Space Models. At the core of our design is the HGMamba block, which integrates message passing, hypergraph scanning & flattening, and bidirectional state space modeling (Bi-SSM), enabling the model to retain both relational and contextual cues while remaining computationally efficient. Compared to Transformer and Graph Transformer counterparts, WSI-HGMamba achieves superior performance with up to 7* reduction in FLOPs. Extensive experiments on multiple public and private WSI benchmarks demonstrate that our method provides a scalable, accurate, and efficient solution for slide-level understanding, making it a promising backbone for next-generation pathology AI systems.

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