Cross-Attention Fusion of MRI and Jacobian Maps for Alzheimer's Disease Diagnosis

• URL: http://arxiv.org/abs/2503.00586v1
• Date: Sat, 01 Mar 2025 18:50:46 GMT
• Title: Cross-Attention Fusion of MRI and Jacobian Maps for Alzheimer's Disease Diagnosis
• Authors: Shijia Zhang, Xiyu Ding, Brian Caffo, Junyu Chen, Cindy Zhang, Hadi Kharrazi, Zheyu Wang,
• Abstract summary: We propose a cross-attention fusion framework to model the intrinsic relationship between sMRI intensity and JSM-derived deformations for Alzheimer's disease classification.<n>Cross-attention fusion achieves superior performance, with mean ROC-AUC scores of 0.903 for AD vs. cognitively normal (CN) and 0.692 for mild cognitive impairment (MCI) vs. CN.<n>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)
• Score: 5.955559082904072
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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