Evidential Fusion Network for Multimodal Survival Prediction under Missing Modalities
cs.LG
Submitted: 2026-06-18
Updated: 2026-09-22
License: http://creativecommons.org/licenses/by/4.0/
The gist: Recent multimodal survival prediction models have demonstrated strong predictive performance by leveraging complementary information across modalities.
Terminology
Abstract
Recent multimodal survival prediction models have demonstrated strong predictive performance by leveraging complementary information across modalities. However, such models generally assume data completeness and exhibit limited robustness toward missing modalities, which are frequently encountered in real-world clinical settings. We propose the Evidential Missing Modality Survival Fusion (EMMS) model for multimodal survival prediction under missing modalities. EMMS offers a straightforward, computationally effective approach to survival analysis without requiring a generative phase for missing data. By employing Dempster-Shafer theory and Gaussian Random Fuzzy Numbers for multimodal decision fusion, it considers both aleatoric and epistemic uncertainty alongside modality reliability for fusion. Moreover, the model treats missing modalities as vacuous evidence, preventing interference with available inputs and naturally reflecting increased uncertainty and calibrated predictions. Extensive experiments on four cancer datasets demonstrate state-of-the-art performance while providing calibrated and interpretable uncertainty estimates under incomplete multimodal observations, without introducing additional computational overhead.
Sources
- Multimodal Masked Autoencoders Learn Transferable Representations
- Embracing Aleatoric Uncertainty in Medical Multimodal Learning with Missing Modalities
- DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction
- Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts
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