MADSurv: An Uncertainty-Aware Framework for Multimodal Cancer Survival Analysis.

Multimodal learning significantly improves cancer survival prediction using multiple data types for each patient, including clinical data, pathological images, and genomic data. However, existing works often assume that each data type contributes equally, neglecting potential conflicting or unreliable information between different modalities by simply concatenating features to form a fused representation. In addition, current frameworks are trained only to rank relative risk among patients and cannot quantify the actual likelihood of survival at specific time points, limiting their utility in real-world clinical settings. In this work, we propose the Modality-Aware Discrete-Time Survival (MADSurv) framework to address these gaps. First, it implements an uncertainty-aware attention mechanism in which modality-specific expert encoders learn both predictive features and their own confidence for intelligent data fusion. This approach leads to more robust and personalized predictions by dynamically focusing on the modalities that are most reliable for each individual patient. Second, in addition to a single overall risk score, MADSurv produces a sequence of survival probabilities for discrete yearly intervals. We evaluated our proposed method on five different cancer datasets. In addition to ranking patients' overall risk using the concordance index, we also assessed the accuracy of the model's survival probability estimates on each yearly milestone using the Brier score. Our experimental results demonstrate that MADSurv achieves superior and competitive performance compared to state-of-the-art methods.