Multi-Modal Mamba Modeling for Survival Prediction (M4Survive): Adapting Joint Foundation Model Representations
Journal:
arXiv
Published Date:
Mar 13, 2025
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.
