Brain region-based attention network for Alzheimer’s disease detection from multimodal imaging

In recent years, assisted diagnosis of Alzheimer’s disease (AD) based on brain imaging has become a forefront research area that integrated artificial intelligence. Structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) techniques respectively reveal changes in brain structure and function. Utilizing deep learning technology, common AD features can be learned from a large amount of imaging data to provide technical support for doctors’ diagnostic evaluations. To achieve more precise AD diagnosis, this study proposed an end-to-end network framework. Firstly, the framework divided brain images into 3D brain regions in the form of patches and used an attention CNN for feature extraction. Subsequently, each brain region feature was associated and optimized with a transformer encoder block equipped with multi-head attention mechanism to better represent the brain region interdependencies for AD and normal controls (NC). Finally, through feature fusion under two sub-networks, the method achieved further improvement in diagnostic performance for AD detection. The effectiveness of our method was validated on the ADNI dataset, and patch-based visualization was used to highlight key brain regions of interest in both imaging modalities.