Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation
Enhui Chai, Sicheng Chen, Tianyi Zhang, Chad Wong, Kecheng Huang, Zeyu Liu, Fei Xia

TL;DR
This paper introduces a geometry-aware hybrid hyperbolic-Euclidean model for whole-slide image analysis, improving tissue architecture and cellular morphology representation, and employs a structured state space model with a mixture-of-experts for enhanced classification.
Contribution
It proposes a novel dual geometric space embedding for WSIs, along with a structured sequence model and regional experts, advancing the accuracy of computational pathology methods.
Findings
Outperforms existing MIL methods on seven WSI datasets.
Effectively models hierarchical tissue structures and local morphology.
Enhances slide-level classification accuracy across multiple cancer types.
Abstract
Accurate analysis of histopathological images is critical for disease diagnosis and treatment planning. Whole-slide images (WSIs), which digitize tissue specimens at gigapixel resolution, are fundamental to this process but require aggregating thousands of patches for slide-level predictions. Multiple Instance Learning (MIL) tackles this challenge with a two-stage paradigm, decoupling tile-level embedding and slide-level prediction. However, most existing methods implicitly embed patch representations in homogeneous Euclidean spaces, overlooking the hierarchical organization and regional heterogeneity of pathological tissues. This limits current models' ability to capture global tissue architecture and fine-grained cellular morphology. To address this limitation, we introduce a hybrid hyperbolic-Euclidean representation that embeds WSI features in dual geometric spaces, enabling…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
