SurvMamba: State Space Model with Multi-grained Multi-modal Interaction for Survival Prediction

TL;DR

SurvMamba introduces a hierarchical state space model with multi-grained multi-modal interaction for improved survival prediction from complex biomedical data, effectively capturing detailed intra-modal features and inter-modal fusion with lower computational complexity.

Contribution

The paper proposes SurvMamba, a novel hierarchical state space model with modules for multi-grained intra-modal interaction and inter-modal fusion, enhancing survival prediction accuracy and efficiency.

Findings

Outperforms existing methods on five TCGA datasets

Achieves better accuracy with lower computational cost

Effectively captures detailed local and global features

Abstract

Multi-modal learning that combines pathological images with genomic data has significantly enhanced the accuracy of survival prediction. Nevertheless, existing methods have not fully utilized the inherent hierarchical structure within both whole slide images (WSIs) and transcriptomic data, from which better intra-modal representations and inter-modal integration could be derived. Moreover, many existing studies attempt to improve multi-modal representations through attention mechanisms, which inevitably lead to high complexity when processing high-dimensional WSIs and transcriptomic data. Recently, a structured state space model named Mamba emerged as a promising approach for its superior performance in modeling long sequences with low complexity. In this study, we propose Mamba with multi-grained multi-modal interaction (SurvMamba) for survival prediction. SurvMamba is implemented with a Hierarchical Interaction Mamba (HIM) module that facilitates efficient intra-modal interactions at different granularities, thereby capturing more detailed local features as well as rich global representations. In addition, an Interaction Fusion Mamba (IFM) module is used for cascaded inter-modal interactive fusion, yielding more comprehensive features for survival prediction. Comprehensive evaluations on five TCGA datasets demonstrate that SurvMamba outperforms other existing methods in terms of performance and computational cost.