SlideMamba：基于熵的自适应融合GNN与Mamba用于数字病理学中增强表示学习
SlideMamba: Entropy-Based Adaptive Fusion of GNN and Mamba for Enhanced Representation Learning in Digital Pathology
AI总结：
提出SlideMamba框架，融合Mamba与GNN，利用熵自适应融合策略，在WSI基因融合预测任务中优于现有方法，PRAUC达0.751。
摘要：
计算病理学的进展日益依赖于从全切片图像（WSIs）中提取有意义的表示，以支持各种临床和生物学任务。在本研究中，我们提出了一个可泛化的深度学习框架，将Mamba架构与图神经网络（GNNs）集成，用于增强WSI分析。我们的方法旨在捕捉局部空间关系和长距离上下文依赖，为数字病理学分析提供灵活的架构。Mamba模块擅长捕捉长距离全局依赖，而GNNs强调细粒度的短距离空间交互。为了有效结合这些互补信号，我们引入了一种自适应融合策略，该策略使用基于熵的置信度加权机制。这种方法通过根据局部与全局信息对不同下游任务的上下文重要性，为预测更自信（较低熵）的分支分配更高权重，从而动态平衡两个分支的贡献。我们在一个代表性任务上展示了我们方法的实用性：从WSIs预测基因融合和突变状态。我们的框架SlideMamba实现了0.751 ± 0.05的精确率-召回率曲线下面积（PRAUC），优于MIL（0.491 ± 0.042）、Trans-MIL（0.39 ± 0.017）、仅Mamba（0.664 ± 0.063）、仅GNN（0.748 ± 0.091）以及先前类似工作GAT-Mamba（0.703 ± 0.075）。SlideMamba在ROC AUC（0.738 ± 0.055）、灵敏度（0.662 ± 0.083）和特异性（0.725 ± 0.094）方面也取得了有竞争力的结果。这些结果突显了集成架构的优势，并通过所提出的基于熵的自适应融合策略得到增强，表明其在计算病理学中空间分辨预测建模任务中具有广阔的应用潜力。
英文摘要：
Advances in computational pathology increasingly rely on extracting meaningful representations from Whole Slide Images (WSIs) to support various clinical and biological tasks. In this study, we propose a generalizable deep learning framework that integrates the Mamba architecture with Graph Neural Networks (GNNs) for enhanced WSI analysis. Our method is designed to capture both local spatial relationships and long-range contextual dependencies, offering a flexible architecture for digital pathology analysis. Mamba modules excels in capturing long-range global dependencies, while GNNs emphasize fine-grained short-range spatial interactions. To effectively combine these complementary signals, we introduce an adaptive fusion strategy that uses an entropy-based confidence weighting mechanism. This approach dynamically balances contributions from both branches by assigning higher weight to the branch with more confident (lower-entropy) predictions, depending on the contextual importance of local versus global information for different downstream tasks. We demonstrate the utility of our approach on a representative task: predicting gene fusion and mutation status from WSIs. Our framework, SlideMamba, achieves an area under the precision recall curve (PRAUC) of 0.751 \pm 0.05, outperforming MIL (0.491 \pm 0.042), Trans-MIL (0.39 \pm 0.017), Mamba-only (0.664 \pm 0.063), GNN-only (0.748 \pm 0.091), and a prior similar work GAT-Mamba (0.703 \pm 0.075). SlideMamba also achieves competitive results across ROC AUC (0.738 \pm 0.055), sensitivity (0.662 \pm 0.083), and specificity (0.725 \pm 0.094). These results highlight the strength of the integrated architecture, enhanced by the proposed entropy-based adaptive fusion strategy, and suggest promising potential for application of spatially-resolved predictive modeling tasks in computational pathology.
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