医疗领域鲁棒多模态表征学习
Robust Multimodal Representation Learning in Healthcare
AI总结：
本文提出双流特征去相关框架，通过结构因果分析处理医疗多模态数据中的系统性偏差，提升模型泛化能力，实验验证在MIMIC-IV、eICU和ADNI数据集上的性能提升。
摘要：
医疗多模态表征学习旨在将异构数据整合为统一的患者表示以支持临床结果预测。然而，真实世界医疗数据集通常包含来自多个来源的系统性偏差，这对医疗多模态表征学习提出了重大挑战。现有方法通常专注于有效的多模态融合，忽视了影响泛化能力的固有偏见特征。为解决这些挑战，我们提出了一种双流特征去相关框架，通过引入由潜在混杂因素引入的结构因果分析来识别和处理偏见。我们的方法采用因果偏见去相关框架，结合双流神经网络，将因果特征与虚假相关性分离，利用广义交叉熵损失和互信息最小化实现有效去相关。该框架模型无关，可集成到现有医疗多模态学习方法中。在MIMIC-IV、eICU和ADNI数据集上的全面实验显示了一致的性能提升。
英文摘要：
Medical multimodal representation learning aims to integrate heterogeneous data into unified patient representations to support clinical outcome prediction. However, real-world medical datasets commonly contain systematic biases from multiple sources, which poses significant challenges for medical multimodal representation learning. Existing approaches typically focus on effective multimodal fusion, neglecting inherent biased features that affect the generalization ability. To address these challenges, we propose a Dual-Stream Feature Decorrelation Framework that identifies and handles the biases through structural causal analysis introduced by latent confounders. Our method employs a causal-biased decorrelation framework with dual-stream neural networks to disentangle causal features from spurious correlations, utilizing generalized cross-entropy loss and mutual information minimization for effective decorrelation. The framework is model-agnostic and can be integrated into existing medical multimodal learning methods. Comprehensive experiments on MIMIC-IV, eICU, and ADNI datasets demonstrate consistent performance improvements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
