Decouple, Reorganize, and Fuse: A Multimodal Framework for Cancer Survival Prediction

Cancer survival analysis commonly integrates information across diverse medical modalities to make survival-time predictions. Existing methods primarily focus on extracting different decoupled features of modalities and performing fusion operations such as concatenation, attention, and Mixture-of-Experts (MoE)-based fusion. However, these methods still face two key challenges: 1) fixed fusion schemes (concatenation and attention) can lead to model over-reliance on predefined feature combinations, limiting the dynamic fusion of decoupled features; and 2) in MoE-based fusion methods, each expert network handles separate decoupled features, which limits information interaction among the decoupled features. To address these challenges, we propose a novel Decoupling-Reorganization-Fusion framework (DeReF), which devises a random feature reorganization strategy between modalities decoupling and dynamic MoE fusion modules. Its advantages are: 1) it increases the diversity of feature combinations and granularity, enhancing the generalization ability of the subsequent expert networks; and 2) it overcomes the problem of information closure and helps expert networks better capture information among decoupled features. Additionally, we incorporate a regional cross-attention network within the modality decoupling module to improve the representation quality of decoupled features. Extensive experimental results on our in-house Liver Cancer (LC) and three widely used public datasets from The Cancer Genome Atlas (TCGA) confirm the effectiveness of our proposed method. Codes are available at https://github.com/ZJUMAI/DeReF.