用于阿尔茨海默病诊断的MRI与Jacobian图谱的交叉注意力融合
Cross-Attention Fusion of MRI and Jacobian Maps for Alzheimer's Disease Diagnosis
- Johns Hopkins University（约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出一种交叉注意力融合框架，结合结构MRI强度与Jacobian行列式图谱的变形信息进行阿尔茨海默病分类，在ADNI数据集上取得高AUC且模型参数量极低。
AI中文摘要：
阿尔茨海默病（AD）的早期诊断对于在不可逆的神经退行性病变发生前进行干预至关重要。结构磁共振成像（sMRI）被广泛用于AD诊断，但传统的深度学习方法主要依赖基于强度的特征，这需要大型数据集才能捕捉细微的结构变化。Jacobian行列式图谱（JSM）通过编码局部脑变形提供互补信息，然而现有的多模态融合策略未能将这些特征与sMRI充分整合。我们提出了一种交叉注意力融合框架，以对sMRI强度与JSM衍生的变形之间的内在关系进行建模，从而进行AD分类。使用阿尔茨海默病神经影像学计划（ADNI）数据集，我们将交叉注意力、成对自注意力和瓶颈注意力与四种预训练的3D图像编码器进行了比较。交叉注意力融合实现了卓越的性能，在AD与认知正常（CN）对比中的平均ROC-AUC得分为0.903（+/-0.033），在轻度认知障碍（MCI）与CN对比中的平均ROC-AUC得分为0.692（+/-0.061）。尽管性能强劲，我们的模型仍保持高效，仅有156万个参数——比ResNet-34（63M）和Swin UNETR（61.98M）少40倍以上。这些发现证明了交叉注意力融合在提高AD诊断准确性的同时保持计算效率的潜力。
英文摘要：
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.
