ArticleFrontiers in neurology
APLG-Net: an anatomy-guided local-global hybrid network with progression-aware supervision for structural MRI-based NC/MCI/AD classification.
Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Structural MRI-based Alzheimer's disease classification remains challenging due to subtle anatomical variations and the intermediate nature of mild cognitive impairment (MCI). Methods: We propose APLG-Net, an anatomy-guided local-global hybrid network with progression-aware supervision for NC/MCI/AD classification. The model integrates a global whole-brain encoder and a local ROI-based encoder, followed by cross-attention fusion and vector-gated integration. An ordinal supervision strategy is introduced to model disease progression. Results: On the ADNI dataset, APLG-Net achieves 87.1% accuracy, 86.4% balanced accuracy, 86.8% Macro-F1, and 85.6% MCI F1, outperforming CNN-based, Transformer-based, and hybrid baselines. Discussion: The results demonstrate that incorporating anatomical priors, local-global feature interaction, and ordinal supervision significantly improves MCI discrimination and overall classification robustness.
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