Temporal‐multimodal consistency alignment for Alzheimer's cognitive assessment prediction

In this work, we proposed a novel framework that unifies multimodality fusion with dual-granularity alignment for cognitive assessment forecasting. Our approach utilizes a temporal-multimodal consistency alignment strategy, which effectively synchronizes various modalities within a unified latent space. Furthermore, the innovative HMF block we developed capitalizes on the inherent relationships and dependencies between modalities to optimize data integration. Extensive numerical results on five cognitive assessment scores, supported by detailed visualizations demonstrate the superior performance of our approach compared to existing methods. Our code has been released, and it is available at https://github.com/IcecreamArtist/MM_DURA.