基于拓扑感知排序的图Mamba生存分析
Graph Mamba Survival Analysis Based on Topology-Aware ordering
AI总结：
针对WSI生存分析中Mamba模型对输入顺序敏感及单向架构限制空间结构利用的问题，提出基于拓扑感知排序的图Mamba框架TopoMamSurv，通过TAO策略、双向Mamba模块和GCN集成实现高效长程依赖建模与双向空间上下文建模。
摘要：
在计算病理学中，全切片图像（WSI）生存分析对于患者预后评估至关重要，但面临多项技术挑战。尽管Transformer通过其自注意力机制捕获长程依赖，但其$O(N^2)$时间复杂度在大规模WSI图结构中造成严重计算瓶颈。Mamba模型以线性复杂度突破了Transformer的计算瓶颈。然而，由于Mamba对输入数据顺序的高度敏感性，图Mamba中传统的节点排序方法（如基于节点度或子图大小的方法）未能充分考虑图数据的拓扑连通性，从而限制了Mamba序列建模的性能。此外，其单向架构无法利用图像的双向空间结构。为解决这些挑战，本文提出一种基于拓扑感知排序的新型图Mamba生存分析框架（TopoMamSurv），以适应Mamba的序列敏感性。我们的可视化实验进一步证实，通过拓扑感知排序（TAO）策略提取的节点确实表现出更高的相似性。此外，我们设计了双向Mamba模块并集成图卷积网络（GCN），以实现图像的双向空间上下文建模，形成“局部聚合-全局捕获”的分层特征学习架构。该框架通过TAO、双向语义建模和分层特征融合的系统设计，有效调和了WSI分析中长程依赖建模、计算效率和空间结构利用之间的矛盾。该框架在五个TCGA数据集上验证了其全面的性能优势。
英文摘要：
In computational pathology, Whole Slide Images (WSIs) survival analysis is crucial for patient prognosis assessment, but it faces multiple technical challenges. Although the Transformer captures long-range dependencies through its self-attention mechanism, its $O(N^2)$ time complexity causes a severe computational bottleneck in large-scale WSIs graph structures. The Mamba model breaks through the Transformer's computational bottleneck with linear complexity. But, owing to Mamba's high sensitivity to the order of input data, traditional node sorting methods in Graph Mamba, such as those based on node degree or subgraph size, fail to adequately account for the topological connectivity of graph data. This inadequacy consequently restricts the performance of Mamba's sequential modeling. Moreover, its unidirectional architecture cannot leverage the bidirectional spatial structure of images. To address these challenges, this paper proposes a novel Graph Mamba survival analysis framework based on topology-aware ordering (TopoMamSurv) to adapt to the sequential sensitivity of Mamba. Our visualization experiments further confirmed that the nodes extracted through the topology-aware ordering (TAO) strategy indeed exhibit higher similarity. Furthermore, we designed a bidirectional Mamba module and integrated a Graph Convolutional Network (GCN) to achieve bidirectional spatial context modeling of images, forming a hierarchical feature learning architecture for "local aggregation - global capture." This framework effectively reconciles the contradiction between long-range dependency modeling, computational efficiency, and spatial structure utilization in WSIs analysis through its systematic design of TAO, bidirectional semantic modeling, and hierarchical feature fusion. This framework has been validated for its comprehensive performance advantage on five TCGA datasets.
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。
