CROSS-ATTENTION FUSION OF STRUCTURAL AND DEFORMATION-BASED MRI FEATURES FOR ALZHEIMER'S DISEASE CLASSIFICATION

Accurate assessment of Alzheimer's disease (AD) using neuroimaging is important for understanding disease progression and supporting clinical research. Structural MRI (sMRI) is widely used for this purpose, but most deep learning approaches primarily rely on intensity-based features, which may not fully capture subtle morphological variations. Jacobian determinant maps (JSM) provide complementary information by describing localized brain deformations; however, conventional multimodal fusion strategies such as early or late fusion may not fully capture the interactions between these modalities. To address this, we explore a cross-attention fusion approach that explicitly models the relationship between sMRI intensity and JSM-derived deformation features for AD classification. Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we compare cross-attention with pairwise self-attention and four baseline fusion methods. The proposed approach achieved mean ROC-AUC scores of 0.903 (0.033) for AD vs. cognitively normal (CN) and 0.692 (0.061) for mild cognitive impairment (MCI) vs. CN under five-fold cross-validation. The proposed approach achieved the best performance among fusion strategies and achieved higher or comparable performance with only 1.56 million parameters. These findings suggest that cross-attention fusion may serve as a potentially useful approach for integrating structural and deformation-based MRI features in AD classification tasks.