ME-Mamba：面向多模态生存分析的高效知识捕获与融合的多专家Mamba
ME-Mamba: Multi-Expert Mamba with Efficient Knowledge Capture and Fusion for Multimodal Survival Analysis
AI总结：
本文提出ME-Mamba多专家系统，通过病理专家、基因组学专家和协同专家分别进行单模态特征提取与基于最优传输和最大均值差异的跨模态融合，在TCGA五个数据集上以较低计算复杂度实现了最先进的多模态生存分析性能。
摘要：
基于全切片图像（WSIs）的生存分析在癌症研究中至关重要。尽管已取得显著成功，病理图像通常仅提供切片级标签，这阻碍了从十亿像素级WSIs中学习具有判别性的表示。随着高通量测序技术的快速发展，整合病理图像与基因组学数据的多模态生存分析已成为一种有前景的方法。我们提出了一种多专家Mamba（ME-Mamba）系统，该系统能够捕获具有判别性的病理和基因组特征，同时实现两种模态的高效整合。该方法在避免丢失单个模态关键信息的前提下实现互补信息融合，从而促进准确的癌症生存分析。具体而言，我们首先引入病理专家和基因组学专家分别处理单模态数据。两个专家均采用Mamba架构设计，并结合常规扫描与基于注意力的扫描机制，使其能够从包含大量冗余或无关信息的长实例序列中提取判别性特征。其次，我们设计了一个协同专家负责模态融合。该专家通过最优传输显式学习两种模态之间的token级局部对应关系，并通过基于最大均值差异的全局跨模态融合损失隐式增强分布一致性。融合后的特征表示随后被传递至Mamba骨干网络进行进一步整合。通过病理专家、基因组学专家和协同专家的协作，我们的方法以相对较低的计算复杂度实现了稳定且准确的生存分析。在癌症基因组图谱（TCGA）的五个数据集上的大量实验结果表明，我们达到了最先进的性能。
英文摘要：
Survival analysis using whole-slide images (WSIs) is crucial in cancer research. Despite significant successes, pathology images typically only provide slide-level labels, which hinders the learning of discriminative representations from gigapixel WSIs. With the rapid advancement of high-throughput sequencing technologies, multimodal survival analysis integrating pathology images and genomics data has emerged as a promising approach. We propose a Multi-Expert Mamba (ME-Mamba) system that captures discriminative pathological and genomic features while enabling efficient integration of both modalities. This approach achieves complementary information fusion without losing critical information from individual modalities, thereby facilitating accurate cancer survival analysis. Specifically, we first introduce a Pathology Expert and a Genomics Expert to process unimodal data separately. Both experts are designed with Mamba architectures that incorporate conventional scanning and attention-based scanning mechanisms, allowing them to extract discriminative features from long instance sequences containing substantial redundant or irrelevant information. Second, we design a Synergistic Expert responsible for modality fusion. It explicitly learns token-level local correspondences between the two modalities via Optimal Transport, and implicitly enhances distribution consistency through a global cross-modal fusion loss based on Maximum Mean Discrepancy. The fused feature representations are then passed to a mamba backbone for further integration. Through the collaboration of the Pathology Expert, Genomics Expert, and Synergistic Expert, our method achieves stable and accurate survival analysis with relatively low computational complexity. Extensive experimental results on five datasets in The Cancer Genome Atlas (TCGA) demonstrate our state-of-the-art performance.
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