隣接認識型モダリティ回復に基づく不完全マルチモーダル脳疾患診断

Multi-modal learning is extensively applied to diagnose brain diseases such as epilepsy and Alzheimer's disease. However, incomplete multi-modal data, where some modalities are unavailable or difficult to collect, limits the effectiveness of conventional methods. Additionally, existing approaches often overlook semantic relationships between neighbors with the same-label and latent information in missing modalities. To address these challenges, we propose an adjacent-aware distillation recovery framework designed for incomplete multi-modal learning, with a focus on diagnosing representative brain diseases, i.e. epilepsy and Alzheimer's disease. The key novelty of our framework lies in its joint design of adjacent-aware modality recovery and multi-modal representation learning in a single end-to-end pipeline. Specifically, we introduce a label-guided adjacent-aware recovery module that uses a self-attention mechanism to exploit neighbor semantics and generate distribution-consistent features for high-quality modality reconstruction. The recovered features are then refined through a knowledge distillation pathway into a modality generator, enhancing generalization under severe data incompleteness. For multi-modal representation learning, the recovered modality information is fused with the original incomplete information to enhance feature extraction and representation. Extensive experiments demonstrate the effectiveness of our method in diagnosing epilepsy and Alzheimer's disease.