EsurvFusion: An Evidential Multimodal Survival Fusion Model Based on Epistemic Random Fuzzy Sets

Multimodal survival analysis aims to combine heterogeneous data sources to improve the prediction quality of survival outcomes. However, this task is particularly challenging due to high heterogeneity and noise across data sources. Additionally, the exact survival time is often censored (partially known) due to incomplete event observation. To address the above challenges, we propose a novel interpretable evidential multimodal survival fusion model, EsurvFusion. This model is designed to combine multimodal data at the decision level using Epistemic Random Fuzzy Sets that jointly handle both data and model uncertainty while incorporating modality-level reliability. Specifically, EsurvFusion first models unimodal data with newly introduced Gaussian random fuzzy numbers, producing possible unimodal survival predictions along with corresponding aleatory and epistemic uncertainty. It then estimates modality-level reliability through a reliability discounting layer to correct the misleading impact of noisy data modalities. Finally, a multimodal evidence fusion layer is introduced to combine the discounted predictions, revealing modality-level influence based on the learned reliability coefficients. Extensive experiments on four multimodal cancer survival datasets demonstrate the effectiveness of our model in handling highly heterogeneous data, establishing a new state-of-the-art performance on several benchmarks.