文献类型：期刊文献

英文题名：AT-Attn: Temporal-Aware Cross-Attention for Longitudinal Multimodal Alzheimer’s Disease Diagnosis

作者：Du, Xinyue[1]; Liu, Yibo[2]; Zhou, Zhenglei[3]; Yao, Xuancheng[2]; Zhong, Weimin[1]; Chen, Qiuhui[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, China; [2] School of Computer Science, Shanghai Jiao Tong University, China; [3] Tencent, China

年份：2026

外文期刊名：arXiv

收录：EI(收录号：20260429932)

语种：英文

外文关键词：Information fusion - Magnetic resonance imaging - Medical imaging - Neurodegenerative diseases - Plasma diagnostics

摘要：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. Copyright ? 2026, The Authors. All rights reserved.