Multi-View Hilbert Curve-Based Hierarchical Information Aggregation for Incomplete Multimodal Alzheimer's Disease Diagnosis

Timely identification of Alzheimer's disease (AD) benefits from combining neuroimaging, fluid biomarkers, and cognitive assessments, yet in practice one or more modalities are often unavailable due to various factors such as cost, patient compliance, and procedural risks. Furthermore, conventional convolutional neural network (CNN) architectures and even Transformer-based models struggle to efficiently capture both local and global dependencies, especially when dealing with high-dimensional and highly heterogeneous medical data. In this study, we introduce a novel hierarchical information aggregation and dynamic fusion (HI-AD) framework for incomplete multimodal AD diagnosis. Our method couples a multi-view Hilbert curve-guided Mamba block with hierarchical spatial feature extraction to retain spatial continuity, model long-range dependencies, and integrate local context in neuroimaging data. To balance semantic alignment and modality-specific information, we propose a unified mutual information-driven learning objective with an active confidence evaluation mechanism, thereby preventing modality collapse and promoting robust representation learning. Extensive experiments on real-world datasets validate that our HI-AD framework consistently outperforms existing state-of-the-art methods across a diverse range of modality-missing scenarios, establishing an effective and generalizable solution for early-stage AD screening in heterogeneous clinical data environments.