Cross-Attention Fusion of MRI and Jacobian Maps for Alzheimer's Disease Diagnosis
Journal:
arXiv
Published Date:
Mar 1, 2025
Abstract
Early diagnosis of Alzheimer's disease (AD) is critical for intervention
before irreversible neurodegeneration occurs. Structural MRI (sMRI) is widely
used for AD diagnosis, but conventional deep learning approaches primarily rely
on intensity-based features, which require large datasets to capture subtle
structural changes. Jacobian determinant maps (JSM) provide complementary
information by encoding localized brain deformations, yet existing multimodal
fusion strategies fail to fully integrate these features with sMRI. We propose
a cross-attention fusion framework to model the intrinsic relationship between
sMRI intensity and JSM-derived deformations for AD classification. Using the
Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we compare
cross-attention, pairwise self-attention, and bottleneck attention with four
pre-trained 3D image encoders. Cross-attention fusion achieves superior
performance, with 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. Despite its strong performance, our model remains highly
efficient, with only 1.56 million parameters--over 40 times fewer than
ResNet-34 (63M) and Swin UNETR (61.98M). These findings demonstrate the
potential of cross-attention fusion for improving AD diagnosis while
maintaining computational efficiency.
