ResGSNet: Enhanced local attention with Global Scoring Mechanism for the early detection and treatment of Alzheimer's Disease.

Recently, Transformer has been widely used in medical imaging analysis for its competitive potential when given enough data. However, Transformer conducts attention on a global scale by utilizing self-attention mechanisms across all input patches, thereby requiring substantial computational power and memory, especially when dealing with large 3D images such as MRI images. In this study, we proposed Residual Global Scoring Network (ResGSNet), a novel architecture combining ResNet with Global Scoring Module (GSM), achieving high computational efficiency while incorporating both local and global features. First, our proposed GSM utilized local attention to conduct information exchange within local brain regions, subsequently assigning global scores to each of these local regions, demonstrating the capability to encapsulate local and global information with reduced computational burden and superior performance compared to existing methods. Second, we utilized Grad-CAM＋＋ and the Attention Map to interpret model predictions, uncovering brain regions related to Alzheimer's Disease (AD) Detection. Third, our extensive experiments on the ADNI dataset show that our proposed ResGSNet achieved satisfactory performance with 95.1% accuracy in predicting AD, a 1.3% increase compared to state-of-the-art methods, and 93.4% accuracy for Mild Cognitive Impairment (MCI). Our model for detecting MCI can potentially serve as a screening tool for identifying individuals at high risk of developing AD and allow for early intervention. Furthermore, the Grad-CAM＋＋ and Attention Map not only identified brain regions commonly associated with AD and MCI but also revealed previously undiscovered regions, including putamen, cerebellum cortex, and caudate nucleus, holding promise for further research into the etiology of AD.