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arXiv Machine Learning By Yucheng Xing, Hailan Mo, Zi Wang, Ling Huang, Mengling Feng

Evidential Fusion Network for Multimodal Survival Prediction under Missing Modalities

Read the original on arXiv Machine Learning →

The paper introduces the Evidential Missing Modality Survival Fusion (EMMS) model, which predicts survival outcomes using multimodal data even when some modalities are missing. EMMS applies Dempster‑Shafer theory and Gaussian Random Fuzzy Numbers to fuse information, accounting for both aleatoric and epistemic uncertainty and the reliability of each modality. Experiments on four cancer datasets show that EMMS achieves state‑of‑the‑art performance while providing calibrated, interpretable uncertainty estimates without extra computational cost.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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