Leveraging hemispheric asymmetry in structural MRI with an attention-guided 3D CNN for early prediction of...

Early identification of mild cognitive impairment (MCI) progressing to Alzheimer's disease (AD) is of paramount importance. Despite the notable advances in deep learning in this domain, current approaches are largely based on global brain analysis and often overlook the hemispheric asymmetry, which is a critical biomarker for AD progression. Although longitudinal studies can capture temporal dynamics, their clinical feasibility is constrained by the need for multiple follow-up visits. To address this issue, we propose HemiNet, a lightweight 3D convolutional neural network based on hemispheric difference analysis, enabling accurate prediction of MCI progression from structural MRI at a single time point. HemiNet is designed with three key modules. First, the asymmetry discrepancy mining strategy is employed to quantify interhemispheric structural differences, derive disease-specific biomarkers, and effectively capture multi-level asymmetry features. Second, the contralateral hemispheric fusion mechanism is designed to adaptively unify bilateral features through discrepancy-aware gating combined with depthwise separable convolution, thus strengthening asymmetry patterns indicative of AD. Finally, the pathology focal attention mechanism is applied with sequential channel-spatial attention to highlight pivotal pathological regions, such as the hippocampus and temporal lobe, thereby enhancing the discriminative capacity of the learned features. Extensive experiments and cross-validation on the ADNI dataset demonstrate that HemiNet achieves an AUC of 84.01% and an accuracy of 78.19% for MCI prediction. This study validates the value of hemispheric asymmetry analysis for early AD detection and presents an efficient, lightweight, and interpretable method for MCI progression prediction from a single scan.