This paper tackles the problem of time-to-event counterfactual survival prediction, aiming to optimize individualized survival outcomes in the presence of heterogeneity and censored data. We propose CURE, a framework that advances counterfactual survival modeling via comprehensive multimodal embedding and latent subgroup retrieval. CURE integrates clinical, paraclinical, demographic, and multi-omics information, which are aligned and fused through cross-attention mechanisms. Complex multi-omics signals can be adaptively refined using a mixture-of-experts architecture, emphasizing the most informative omics components. Building upon this representation, CURE implicitly retrieves patient-specific latent subgroups that capture both baseline survival dynamics and treatment-dependent variations. Experimental results on METABRIC and TCGA-LUAD datasets demonstrate that proposed CURE model consistently outperforms strong baselines in survival analysis, evaluated using the Time-dependent Concordance Index ($C^{td}$) and Integrated Brier Score (IBS). These findings highlight the potential of CURE to enhance multimodal understanding and serve as a foundation for future treatment recommendation models. All code and related resources are publicly available to facilitate the reproducibility https://github.com/L2R-UET/CURE.

翻译：本文针对事件时间反事实生存预测问题，旨在存在异质性和删失数据的情况下优化个体化生存结果。我们提出CURE框架，该框架通过全面的多模态嵌入和潜在亚组检索推进反事实生存建模。CURE整合了临床、辅助临床、人口统计学及多组学信息，这些信息通过交叉注意力机制进行对齐与融合。复杂的多组学信号可采用专家混合架构进行自适应优化，以突出信息最丰富的组学成分。基于此表征，CURE能够隐式检索患者特定的潜在亚组，这些亚组同时捕捉基线生存动态和治疗依赖性变异。在METABRIC和TCGA-LUAD数据集上的实验结果表明，所提出的CURE模型在生存分析中持续优于强基线模型，评估指标采用时间依赖性一致性指数（$C^{td}$）和综合Brier评分（IBS）。这些发现凸显了CURE在增强多模态理解方面的潜力，并为未来治疗推荐模型奠定了基础。所有代码及相关资源已公开，以促进可重复性研究：https://github.com/L2R-UET/CURE。