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

• URL: http://arxiv.org/abs/2503.10057v1
• Date: Thu, 13 Mar 2025 05:18:32 GMT
• Title: Multi-Modal Mamba Modeling for Survival Prediction (M4Survive): Adapting Joint Foundation Model Representations
• Authors: Ho Hin Lee, Alberto Santamaria-Pang, Jameson Merkov, Matthew Lungren, Ivan Tarapov,
• Abstract summary: M4Survive is a novel framework that learns joint foundation model representations using efficient adapter networks.<n>By leveraging Mamba-based adapters, M4Survive enables efficient multi-modal learning while preserving computational efficiency.<n>This work underscores the potential of foundation model-driven multi-modal fusion in advancing precision oncology and predictive analytics.
• Score: 0.6990493129893112
• License: http://creativecommons.org/licenses/by/4.0/
• 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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