Multi-modal Tensor Fusion for Alzheimer’s Disease Recognition

Abstract

Accurate and early diagnosis of Alzheimer’s disease (AD) is critical for effective intervention, disease monitoring, and patient care. Traditional diagnostic approaches rely on a single modality, such as clinical assessments, neuroimaging, or genetic markers, which may fail to capture the complex, multifaceted nature of AD. Multimodal learning has therefore been explored to integrate complementary information across data sources. However, conventional fusion strategies, including early feature concatenation and late decision-level fusion, often model modalities independently and fail to capture high-order cross-modal interactions. To address these limitations, we propose a multimodal tensor fusion network (MTFN) that integrates heterogeneous data sources, including visual imagery, demographics, and longitudinal time-series data, to enhance AD recognition. Our approach leverages tensor representations to model intricate cross-modal interactions while preserving structural dependencies within each modality. Experimental results on publicly available AD datasets demonstrate that the proposed method outperforms the accuracy of the state-of-the-art deep learning classification. This work highlights the potential of tensor-based multimodal learning to advance precision medicine for neurodegenerative diseases