用时间感知方法融合脑影像与临床数据，提升阿尔茨海默病诊断准确率。
AT-Attn: Temporal-Aware Cross-Attention for Longitudinal Multimodal Alzheimer's Disease Diagnosis
- 引入时间编码与双向注意力机制，精准捕捉多模态数据的时间动态变化
- 在1520名患者上达到AUC 0.873，显著优于简单融合方法
- 适合需要处理不规则随访数据的医疗AI研究者参考
在纵向阿尔茨海默病（AD）诊断中，临床与影像数据常在不规则时间点采集。将这些多模态观测信息融合可提升诊断效果，但若MRI数据噪声大或间歇缺失，直接融合反而会降低性能。本文提出AT-Attn，一种时间感知的多模态框架，结合变化-时间编码、时间偏置的非对称交叉注意力和门控融合机制，有效整合结构化MRI与纵向临床信息。在包含1,520名患者的ADNI队列上，使用结构化MRI、六个认知量表轨迹和七个静态临床变量，进行患者级五折交叉验证。主模型实现准确率0.719±0.024，宏平均F1 0.721±0.023，ROC-AUC 0.873±0.013，PR-AUC 0.783±0.018，优于单模态及朴素融合基线，且与强表格基线相当。结果表明，时间感知的约束融合策略能有效让结构化MRI提供临床互补信息，助力个体化AD诊断。
原文摘要 · Abstract (English)
In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits. Integrating these multimodal observations may improve diagnostic assessment, but naive fusion can degrade performance when MRI is noisy or intermittently unavailable. We propose AT-Attn, a temporal-aware multimodal framework that combines Change-and-Time encoding, time-biased asymmetric cross-attention, and gated fusion to integrate MRI with longitudinal clinical information. We evaluate AT-Attn on an MRI-retained ADNI cohort of 1,520 patients using structural MRI, six cognitive-scale trajectories, and seven static clinical variables under patient-level five-fold cross-validation. The main asymmetric AT-Attn model achieves accuracy 0.719+/-0.024, macro F1 0.721+/-0.023, ROC-AUC 0.873+/-0.013, and PR-AUC 0.783+/-0.018, outperforming unimodal and naive multimodal fusion baselines while remaining competitive with strong tabular baselines. These results suggest that a temporal-aware and constrained fusion strategy can help structural MRI contribute clinically relevant complementary information for patient-level AD diagnosis support.
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