Evidential Fusion Network for Multimodal Survival Prediction under Missing Modalities

2026-06-18

AI Summary

This study addresses the challenge of missing modalities in clinical multimodal survival prediction, where existing methods often lack robustness. The authors propose the EMMS model, which innovatively treats missing modalities as vacuous evidence and integrates Dempster–Shafer evidence theory with Gaussian random fuzzy numbers. Without imputing missing data, EMMS jointly models epistemic and aleatoric uncertainties and dynamically evaluates modality reliability. Evaluated on four cancer datasets, the method achieves state-of-the-art performance while delivering computationally efficient, well-calibrated, and interpretable uncertainty estimates.

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.

Problem

• multimodal survival prediction
• missing modalities
• data incompleteness
• clinical settings
• robustness

Innovation

• Evidential Fusion
• Missing Modalities
• Survival Prediction
• Uncertainty Quantification
• Dempster-Shafer Theory