MoEMambaMIL: Structure-Aware Selective State Space Modeling for Whole-Slide Image Analysis

• URL: http://arxiv.org/abs/2603.06378v1
• Date: Fri, 06 Mar 2026 15:28:07 GMT
• Title: MoEMambaMIL: Structure-Aware Selective State Space Modeling for Whole-Slide Image Analysis
• Abstract summary: MoEMambaMIL is a structure-aware framework for whole-slide image (WSI) analysis.<n>It integrates region-nested selective scanning with mixture-of-experts (MoE) modeling.<n>MoEMambaMIL achieves the best performance across 9 downstream tasks.
• Score: 0.7898424058509471
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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-experts (MoE) modeling. Leveraging multi-resolution preprocessing, MoEMambaMIL organizes patch tokens into region-aware sequences that preserve spatial containment across resolutions. On top of this structured sequence, we decouple resolution-aware encoding and region-adaptive contextual modeling via a combination of static, resolution-specific experts and dynamic sparse experts with learned routing. This design enables efficient long-sequence modeling while promoting expert specialization across heterogeneous diagnostic patterns. Experiments demonstrate that MoEMambaMIL achieves the best performance across 9 downstream tasks.

Related papers

• SP-MoMamba: Superpixel-driven Mixture of State Space Experts for Efficient Image Super-Resolution [99.56535031145211]
  State space models (SSMs) have emerged as a powerful paradigm for efficient single-image super-resolution (SR)<n>We propose textbfSP-MoMamba, a superpixel-driven mixture of state space experts for content-aware SR.<n>Our core idea is to transform the traditional rigid scanning into a textbfsemantic-level interaction by treating superpixels as fundamental units.
  arXiv  Detail & Related papers  (2026-05-25T14:19:59Z)
• Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation [8.483387656208363]
  Whole-slide images (WSIs) digitize tissue specimens at gigapixel resolution.<n>Current methods implicitly embed patch representations in homogeneous Euclidean spaces.<n>We introduce a hybrid hyperbolic-Euclidean representation that embeds WSI features in dual geometric spaces.
  arXiv  Detail & Related papers  (2026-05-06T17:33:30Z)
• LayerTracer: A Joint Task-Particle and Vulnerable-Layer Analysis framework for Arbitrary Large Language Model Architectures [2.175079806379993]
  This paper proposes LayerTracer, an architecture-agnostic end-to-end analysis framework compatible with any Large Language Models (LLMs) architecture.<n>By extracting hidden states layer-by-layer and mapping them to vocabulary probability distributions, it achieves joint analysis of task particle localization and layer vulnerability quantification.
  arXiv  Detail & Related papers  (2026-04-22T13:38:15Z)
• MS-SSM: A Multi-Scale State Space Model for Efficient Sequence Modeling [60.648359990090846]
  State-space models (SSMs) have recently attention as an efficient alternative to computationally expensive attention-based models for sequence modeling.<n>This paper introduces a multi-scale SSM framework that represents sequence dynamics across multiple resolution and processing each resolution with specialized state-space dynamics.
  arXiv  Detail & Related papers  (2025-12-29T19:36:28Z)
• X-VMamba: Explainable Vision Mamba [0.0]
  State Space Models (SSMs) have emerged as powerful alternatives to Transformers for sequence modeling.<n>We introduce a controllability-based interpretability framework that quantifies how different parts of the input sequence (tokens or patches) influence the internal state dynamics of SSMs.<n>Our framework establishes controllability analysis as a unified, foundational interpretability paradigm for SSMs across all domains.
  arXiv  Detail & Related papers  (2025-11-16T17:18:12Z)
• GCRPNet: Graph-Enhanced Contextual and Regional Perception Network for Salient Object Detection in Optical Remote Sensing Images [68.33481681452675]
  We propose a graph-enhanced contextual and regional perception network (GCRPNet)<n>It builds upon the Mamba architecture to simultaneously capture long-range dependencies and enhance regional feature representation.<n>It performs adaptive patch scanning on feature maps processed via multi-scale convolutions, thereby capturing rich local region information.
  arXiv  Detail & Related papers  (2025-08-14T11:31:43Z)
• Mesh Mamba: A Unified State Space Model for Saliency Prediction in Non-Textured and Textured Meshes [50.23625950905638]
  Mesh saliency enhances the adaptability of 3D vision by identifying and emphasizing regions that naturally attract visual attention.<n>We introduce mesh Mamba, a unified saliency prediction model based on a state space model (SSM)<n>Mesh Mamba effectively analyzes the geometric structure of the mesh while seamlessly incorporating texture features into the topological framework.
  arXiv  Detail & Related papers  (2025-04-02T08:22:25Z)
• FACTS: A Factored State-Space Framework For World Modelling [24.08175276756845]
  We propose a novel recurrent framework, the textbfFACTored textbfState-space (textbfFACTS) model, for spatial-temporal world modelling.<n>The FACTS framework constructs a graph-memory with a routing mechanism that learns permutable memory representations.<n>It consistently outperforms or matches specialised state-of-the-art models, despite its general-purpose world modelling design.
  arXiv  Detail & Related papers  (2024-10-28T11:04:42Z)
• Efficient High-Resolution Visual Representation Learning with State Space Model for Human Pose Estimation [60.80423207808076]
  Capturing long-range dependencies while preserving high-resolution visual representations is crucial for dense prediction tasks such as human pose estimation.<n>We propose the Dynamic Visual State Space (DVSS) block, which augments visual state space models with multi-scale convolutional operations.<n>We build HRVMamba, a novel model for efficient high-resolution representation learning.
  arXiv  Detail & Related papers  (2024-10-04T06:19:29Z)
• MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models [56.37780601189795]
  We propose a framework named MamMIL for WSI analysis. We represent each WSI as an undirected graph. To address the problem that Mamba can only process 1D sequences, we propose a topology-aware scanning mechanism.
  arXiv  Detail & Related papers  (2024-03-08T09:02:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.