arXiv cs.AI· Enhui Chai, Sicheng Chen, Tianyi Zhang, Chad Wong, Kecheng Huang, Zeyu Liu, Fei Xia·· 4 小时前AI 评分25
BatMIL：面向全切片图像表征的几何嵌入状态空间模型
BatMIL: Geometry-Embedding State Space Model for Whole-Slide Image Representation
AI 导读
研究者提出 BatMIL，一种结合双曲-欧氏双重几何空间嵌入的全切片图像（WSI）多示例学习（MIL）方法，互补建模组织层级结构与局部形态细节。模型采用结构化状态空间序列骨干以线性复杂度编码数千个 patch 的长程依赖，并用 mixture-of-experts 模块按特征动态路由 patch。在覆盖六种癌症类型的七个数据集上，BatMIL 优于十五种 SOTA MIL 方法。
正文 · 原文
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 complementary modeling of hierarchical tissue structures and local morphological details. Building on this formulation, we develop BatMIL, an MIL method that leverages both geometric spaces. To model long-range dependencies among thousands of patches, we employ a structured state space sequence model backbone that encodes patch sequences with linear computational complexity. Furthermore, to account for regional heterogeneity, we introduce a mixture-of-experts module that dynamically routes each patch to specialized expert sub-networks based on its features, improving representational capacity while reducing redundant computation. Experiments on seven datasets spanning six cancer types demonstrate that BatMIL achieves outstanding performance against fifteen state-of-the-art MIL approaches, demonstrating that geometry-embedding representation learning offers a promising direction for computational pathology.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.05164 [cs.CV] |
| (or arXiv:2605.05164v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.05164 arXiv-issued DOI via DataCite |
Submission history
From: Sicheng Chen [view email]
[v1]
Wed, 6 May 2026 17:33:30 UTC (10,355 KB)
[v2]
Sat, 24 Oct 2026 19:36:39 UTC (22,742 KB)
来源：arXiv cs.AI · arxiv.org