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arXiv · 2503.10057

Multi-Modal Mamba Modeling for Survival Prediction (M4Survive): Adapting Joint Foundation Model Representations

Ho Hin Lee·Alberto Santamaria-Pang·Jameson Merkov·Matthew Lungren·Ivan Tarapov
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Abstract

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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Ho Hin Lee, Alberto Santamaria-Pang, Jameson Merkov, Matthew Lungren, Ivan Tarapov. 2025-03-13. Multi-Modal Mamba Modeling for Survival Prediction (M4Survive): Adapting Joint Foundation Model Representations. https://arxiv.org/abs/2503.10057

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