Multi-Dimensional Feature Adaptive Gating Network for Multi-View and Whole-Brain Magnetic Resonance Imaging-Based Alzheimer's Disease Classification.
TL;DR
A reproducible and explainable deep learning framework, termed the multidimensional feature adaptive gating network (MDFAG-Net), for sMRI-based AD classification and neuroimaging analysis, which demonstrated competitive classification performance and outperformed representative two-dimensional, three-dimensional, and multibranch comparison models under the same experimental setting.
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder associated with structural brain alterations detectable by structural magnetic resonance imaging (sMRI). Accurate classification of normal control (NC), mild cognitive impairment (MCI), and AD remains challenging because disease-related changes are subtle, spatially distributed across multiple brain regions, and difficult to interpret in a biologically meaningful manner. Existing deep learning methods often rely on either whole-brain volumetric features or two-dimensional slice-level representations, which may limit the integration of complementary structural information and reduce model interpretability. This study presents a reproducible and explainable deep learning framework, termed the multidimensional feature adaptive gating network (MDFAG-Net), for sMRI-based AD classification and neuroimaging analysis. The framework integrates multidimensional structural MRI representations by combining 2D slice-level features extracted from sagittal, coronal, and axial views using a Vision Transformer backbone with global whole-brain features extracted using a three-dimensional convolutional neural network. An Enhanced Sparse Multiple Instance Learning module is employed to identify informative slices and suppress redundant information in long slice sequences, while an adaptive gating module dynamically fuses multiview and whole-brain features according to sample-specific feature contributions. The framework was evaluated on 842 subjects from the Alzheimer's Disease Neuroimaging Initiative dataset for NC/MCI/AD classification. MDFAG-Net demonstrated competitive classification performance and outperformed representative two-dimensional, three-dimensional, and multibranch comparison models under the same experimental setting. The model achieved an area under the receiver operating characteristic curve of 0.798. Furthermore, Grad-CAM and DeepSHAP analyses revealed anatomically relevant regions associated with AD progression, including the hippocampus, amygdala, posterior cingulate gyrus, parahippocampal gyrus, and ventricular system.