融合多模态数据，提升生存预测中的反事实分析能力。
Counterfactual Understanding via Retrieval-aware Multimodal Modeling for Time-to-Event Survival Prediction
- 通过跨注意力机制融合临床、组学等多源信息
- 在METABRIC和TCGA-LUAD上显著优于基线模型
- 适合需个性化治疗推荐的医疗AI研究者
本文针对存在异质性和删失数据的生存时间反事实预测问题，提出CURE框架。该框架通过综合多模态嵌入与潜在亚群检索，整合临床、辅助检查、人口统计及多组学信息，并利用交叉注意力机制对齐与融合。多组学信号通过专家混合架构自适应优化，突出最相关组学成分。在此表示基础上，模型隐式检索捕捉基线生存动态与治疗依赖变化的患者特异性潜在亚群。在METABRIC和TCGA-LUAD数据集上的实验表明，所提CURE模型在时间依赖一致性指数（$C^{td}$）和集成Brier评分（IBS）上持续优于强基线模型。结果凸显了其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基础。所有代码及相关资源已公开以支持可复现性：https://github.com/L2R-UET/CURE。
原文摘要 · Abstract (English)
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.
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