用于组织病理学全切片图像分析的形变注意力图表示学习
Deformable Attention Graph Representation Learning for Histopathology Whole Slide Image Analysis
AI总结：
本文提出一种带形变注意力的图神经网络框架，通过动态加权有向图和学习空间偏移，增强病理全切片图像分析中的空间依赖建模，在四个基准数据集上取得最先进性能。
摘要：
全切片图像（WSIs）和感兴趣区域（ROIs）的准确分类是计算病理学中的一个基本挑战。尽管主流方法常采用多实例学习（MIL），但它们在捕捉组织结构间的空间依赖关系方面存在困难。图神经网络（GNNs）已成为建模实例间关系的一种解决方案，但大多数方法依赖于静态图拓扑，并忽略了组织斑块的物理空间位置。此外，传统注意力机制缺乏特异性，限制了其聚焦于结构相关区域的能力。在本工作中，我们提出了一种新颖的带有形变注意力的GNN框架用于病理图像分析。我们基于斑块特征构建一个动态加权有向图，其中每个节点通过注意力加权边聚合来自其邻居的上下文信息。具体而言，我们结合了由每个斑块的真实坐标引导的可学习空间偏移，使模型能够自适应地关注整个切片中形态学相关的区域。这种设计显著增强了上下文感受野，同时保持了空间特异性。我们的框架在四个基准数据集（TCGA-COAD、BRACS、胃肠化生分级和肠道ROI分类）上达到了最先进的性能，展示了形变注意力在捕捉WSIs和ROIs中复杂空间结构方面的强大能力。
英文摘要：
Accurate classification of Whole Slide Images (WSIs) and Regions of Interest (ROIs) is a fundamental challenge in computational pathology. While mainstream approaches often adopt Multiple Instance Learning (MIL), they struggle to capture the spatial dependencies among tissue structures. Graph Neural Networks (GNNs) have emerged as a solution to model inter-instance relationships, yet most rely on static graph topologies and overlook the physical spatial positions of tissue patches. Moreover, conventional attention mechanisms lack specificity, limiting their ability to focus on structurally relevant regions. In this work, we propose a novel GNN framework with deformable attention for pathology image analysis. We construct a dynamic weighted directed graph based on patch features, where each node aggregates contextual information from its neighbors via attention-weighted edges. Specifically, we incorporate learnable spatial offsets informed by the real coordinates of each patch, enabling the model to adaptively attend to morphologically relevant regions across the slide. This design significantly enhances the contextual field while preserving spatial specificity. Our framework achieves state-of-the-art performance on four benchmark datasets (TCGA-COAD, BRACS, gastric intestinal metaplasia grading, and intestinal ROI classification), demonstrating the power of deformable attention in capturing complex spatial structures in WSIs and ROIs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
