Medical multimodal learning faces significant challenges with missing modalities prevalent in clinical practice. Existing approaches assume equal contribution of modality and random missing patterns, neglecting inherent uncertainty in medical data acquisition. In this regard, we propose the Aleatoric Uncertainty Modeling (AUM) that explicitly quantifies unimodal aleatoric uncertainty to address missing modalities. Specifically, AUM models each unimodal representation as a multivariate Gaussian distribution to capture aleatoric uncertainty and enable principled modality reliability quantification. To adaptively aggregate captured information, we develop a dynamic message-passing mechanism within a bipartite patient-modality graph using uncertainty-aware aggregation mechanism. Through this process, missing modalities are naturally accommodated, while more reliable information from available modalities is dynamically emphasized to guide representation generation. Our AUM framework achieves an improvement of 2.26% AUC-ROC on MIMIC-IV mortality prediction and 2.17% gain on eICU, outperforming existing state-of-the-art approaches.

翻译：医学多模态学习面临临床实践中普遍存在的模态缺失带来的重大挑战。现有方法假设各模态贡献相等且缺失模式随机，忽视了医学数据采集过程中固有的不确定性。为此，我们提出随机不确定性建模（AUM）方法，通过显式量化单模态随机不确定性来处理模态缺失问题。具体而言，AUM将每个单模态表示建模为多元高斯分布，以捕捉随机不确定性并实现模态可靠性的量化。为自适应地聚合所捕获的信息，我们在二分患者-模态图中开发了一种动态消息传递机制，采用不确定性感知的聚合策略。通过这一过程，缺失模态得以自然处理，同时来自可用模态的更可靠信息被动态强化以指导表示生成。我们的AUM框架在MIMIC-IV死亡率预测任务中实现了2.26%的AUC-ROC提升，在eICU数据集上获得2.17%的性能增益，显著优于现有最先进方法。