Attention-Guided Multi-Slice Representation and Variance-Weighted Dictionary Learning for Mri-Based Alzheimer's Disease Classification

Abstract

Alzheimer's disease (AD) classification using magnetic resonance imaging (MRI) is essential for early clinical intervention. However, deep learning approaches often face challenges such as limited medical data, high intraclass variability, and uneven diagnostic contributions from individual MRI slices. To address these issues, we propose a hybrid framework that combines attention-guided multislice deep representation learning with a variance-weighted dictionary learning (VW-DL) classifier. A convolutional neural network first extracts slice-level features, which are aggregated through an attention mechanism to generate a volume-level representation. Class-specific dictionaries are then constructed in the deep feature space, and a variance-based weighting strategy is introduced to suppress unstable feature dimensions during sparse reconstruction. Experiments on the ADNI dataset with strict subject-level data partitioning demonstrate that the proposed method achieves an accuracy of 96.2%, outperforming the baseline attention-guided multiple instance learning (MIL) model. It also maintains a balanced sensitivity (95.2%) and specificity (97.8%), effectively reducing false negatives and showing strong potential for reliable automated AD diagnosis.