# Evidential Fusion Network for Multimodal Survival Prediction under Missing Modalities

arXiv: https://arxiv.org/abs/2606.20757 · HTML: https://mlsanity.com/paper/2606.20757/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 33 references that are in the map.
- First posted on arXiv 18 Jun 2026.

## Builds on (most cited first, 20 of 20)

- [SMIL: Multimodal Learning with Severely Missing Modality](https://mlsanity.com/paper/2103.05677/) (2021)
- [Multi-modal Learning with Missing Modality via Shared-Specific Feature Modelling](https://mlsanity.com/paper/2307.14126/) (2023)
- [Multimodal Whole Slide Foundation Model for Pathology](https://mlsanity.com/paper/2411.19666/) (2024)
- [Multimodal Masked Autoencoders Learn Transferable Representations](https://mlsanity.com/paper/2205.14204/) (2022)
- [Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training](https://mlsanity.com/paper/2209.07098/) (2022)
- [Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction](https://mlsanity.com/paper/2304.06819/) (2023)
- [Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction](https://mlsanity.com/paper/2306.08330/) (2023)
- [M3Care: Learning with Missing Modalities in Multimodal Healthcare Data](https://mlsanity.com/paper/2210.17292/) (2022)
- [Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts](https://mlsanity.com/paper/2410.08245/) (2024)
- [A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
- [Pathology-and-genomics Multimodal Transformer for Survival Outcome Prediction](https://mlsanity.com/paper/2307.11952/) (2023)
- [EmbraceNet: A robust deep learning architecture for multimodal classification](https://mlsanity.com/paper/1904.09078/) (2019)
- [Distilled Prompt Learning for Incomplete Multimodal Survival Prediction](https://mlsanity.com/paper/2503.01653/) (2025)
- [Evidential time-to-event prediction with calibrated uncertainty quantification](https://mlsanity.com/paper/2411.07853/) (2024)
- [Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: general framework and practical models](https://mlsanity.com/paper/2202.08081/) (2022)
- [DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction](https://mlsanity.com/paper/2510.00053/) (2025)
- [An evidential time-to-event prediction model based on Gaussian random fuzzy numbers](https://mlsanity.com/paper/2406.13487/) (2024)
- [Embracing Aleatoric Uncertainty in Medical Multimodal Learning with Missing Modalities](https://mlsanity.com/paper/2601.21950/) (2026)
- [Sparse Task Vector Mixup with Hypernetworks for Efficient Knowledge Transfer in Whole-Slide Image Prognosis](https://mlsanity.com/paper/2603.10526/) (2026)
- [Bridging the Modality Bottleneck in Pathology MIL through Virtual Molecular Staining](https://mlsanity.com/paper/2605.16392/) (2026)

## Abstract (arXiv)

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.