MuGI: Multi-Granularity Interactions of Heterogeneous Biomedical Data for Survival Prediction

Multimodal learning significantly benefits survival analysis for cancer, particularly through the integration of pathological images and genomic data. However, this presents new challenges on how to effectively integrate multi-modal biomedical data. Existing multi-modal survival prediction methods focus on mining the consistency or modality-specific information, failing to capture cross-modal interactions. To address this limitation, attention-based methods are proposed to enhance both the consistency and interactions. However, these methods inevitably introduce redundancy due to the overlapped information of multimodal data. In this paper, we propose a Multi-Granularity Interactions of heterogeneous biomedical data framework (MuGI) for precise survival prediction. MuGI consists of: a) unimodal extractor for exploring preliminary modality-specific information, b) multi-modal optimal features capture (MOFC) for extracting ideal multi-modal rep-resentations, eliminating redundancy through decomposed multi-granularity information, as well as capturing consistency in a common space and enhancing modality-specific features in a private space, and c) multimodal hierarchical interaction for sufficient acquisition of cross-modal correlations and interactions through the cooperation of two Bilateral Cross Attention (BCA) modules. We conduct extensive experiments on three cancer cohorts from the Cancer Genome Atlas (TCGA) database. The experimental results demonstrate that our MuGI achieves the state-of-the-art performance, outperforming both unimodal and multi-modal survival prediction methods.