用多模态Mamba模型融合医学影像与病理信息，提升癌症生存预测准确率。
Multi-Modal Mamba Modeling for Survival Prediction (M4Survive): Adapting Joint Foundation Model Representations
- 通过Mamba适配器动态融合多模态医学基础模型嵌入
- 在多个基准数据集上优于单模态和传统多模态方法
- 适合关注精准肿瘤学与多模态医疗分析的研究者
癌症生存预测需要整合多种影像模态以捕捉肿瘤生物学的复杂交互。传统单模态方法难以利用放射科与病理评估的互补信息。本文提出M4Survive（Multi-Modal Mamba Modeling for Survival Prediction），一种新框架，通过高效的适配器网络学习联合基础模型表征。该方法从基础模型库（如MedImageInsight、BiomedCLIP、Prov-GigaPath、UNI2-h）中动态融合异构嵌入，构建优化用于生存风险估计的相关潜在空间。借助Mamba-based适配器，M4Survive实现高效多模态学习且保持计算效率。在基准数据集上的实验表明，该方法在生存预测准确性上超越单模态及传统静态多模态基线。本工作凸显了基础模型驱动的多模态融合在推动精准肿瘤学与预测分析方面的潜力。
原文摘要 · Abstract (English)
Accurate survival prediction in oncology requires integrating diverse imaging modalities to capture the complex interplay of tumor biology. Traditional single-modality approaches often fail to leverage the complementary insights provided by radiological and pathological assessments. In this work, we introduce M4Survive (Multi-Modal Mamba Modeling for Survival Prediction), a novel framework that learns joint foundation model representations using efficient adapter networks. Our approach dynamically fuses heterogeneous embeddings from a foundation model repository (e.g., MedImageInsight, BiomedCLIP, Prov-GigaPath, UNI2-h), creating a correlated latent space optimized for survival risk estimation. By leveraging Mamba-based adapters, M4Survive enables efficient multi-modal learning while preserving computational efficiency. Experimental evaluations on benchmark datasets demonstrate that our approach outperforms both unimodal and traditional static multi-modal baselines in survival prediction accuracy. This work underscores the potential of foundation model-driven multi-modal fusion in advancing precision oncology and predictive analytics.
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