  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.
