解决医学多模态数据缺失时的生存预测难题，提升模型鲁棒性与可信度。
Evidential Fusion Network for Multimodal Survival Prediction under Missing Modalities
- 用证据理论融合多模态数据，同时处理随机与认知不确定性。
- 在4个癌症数据集上达到当前最佳性能，且预测结果更可靠。
- 适合临床真实场景中存在数据缺失的研究者使用。
近期的多模态生存预测模型通过融合不同模态的互补信息，展现出强大的预测能力。然而，这些模型通常假设数据完整，在实际临床环境中频繁出现的数据缺失情况下表现有限。为此，我们提出一种针对缺失模态的多模态生存预测模型——证据缺失模态生存融合（EMMS）。该模型无需生成缺失数据即可高效进行生存分析，通过引入Dempster-Shafer理论和高斯随机模糊数实现多模态决策融合，综合考虑了随机不确定性、认知不确定性以及各模态的可靠性。此外，模型将缺失模态视为无效证据，避免干扰已有输入，自然体现不确定性上升和校准后的预测结果。在四个癌症数据集上的大量实验表明，该方法在不增加计算开销的前提下，实现了最先进的性能，并提供了可解释且校准良好的不确定性估计。
原文摘要 · Abstract (English)
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.
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