Hypergraph Mamba for Efficient Whole Slide Image Understanding
This provides a scalable and efficient solution for slide-level understanding in pathology AI, addressing computational bottlenecks for medical image analysis.
The paper tackled the challenge of analyzing ultra-high-resolution whole slide images in histopathology by introducing WSI-HGMamba, a framework combining hypergraph neural networks and state space models, which achieved superior performance with up to a 7x reduction in FLOPs compared to Transformer-based methods.
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.