A Hybrid Multi-Scale Attention Convolution and Aging Transformer Network for Alzheimer's Disease Diagnosis

Deep neural networks have been successfully investigated in the computational analysis of structural magnetic resonance imaging (sMRI) data for the diagnosis of dementia, such as Alzheimer's disease (AD). The disease-related changes in sMRI may be different in local brain regions, which have variant structures but with some correlations. In addition, aging increases the risk of dementia. However, it is still challenging to capture the local variations and long-range correlations of different brain regions and make use of the age information for disease diagnosis. To address these problems, we propose a hybrid network with multi-scale attention convolution and aging transformer for AD diagnosis. First, to capture the local variations, a multi-scale attention convolution is proposed to learn the feature maps with multi-scale kernels, which are adaptively aggregated by an attention module. Then, to model the long-range correlations of brain regions, a pyramid non-local block is used on the high-level features to learn more powerful features. Finally, we propose an aging transformer subnetwork to embed the age information into image features and capture the dependencies between subjects at different ages. The proposed method can learn not only the subject-specific rich features but also the inter-subject age correlations in an end-to-end framework. Our method is evaluated with T1-weighted sMRI scans from a large cohort of subjects on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results demonstrate that our method has achieved promising performance for AD-related diagnosis.