DiGAN: Diffusion-Guided Attention Network for Early Alzheimer's Disease Detection

2026-02-02

AI Summary

This study addresses the challenges in early Alzheimer’s disease diagnosis posed by subtle structural brain changes during the prodromal stage and the sparsity and irregular sampling of longitudinal clinical data. To overcome these limitations, the authors propose a novel framework that, for the first time, employs a latent diffusion model to synthesize realistic longitudinal neuroimaging trajectories, thereby enriching temporal context. This is integrated with an attention-guided convolutional network to accurately model pathological progression patterns at irregular time points, enabling joint learning of structural–temporal features that discriminate among cognitively normal individuals, those with mild cognitive impairment, and those with subjective cognitive decline. Experiments on both synthetic data and the real-world ADNI dataset demonstrate that the proposed method significantly outperforms current state-of-the-art approaches, substantially improving early detection performance for Alzheimer’s disease.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Life-Long and Continual Learning
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Early diagnosis of Alzheimer's disease (AD) remains a major challenge due to the subtle and temporally irregular progression of structural brain changes in the prodromal stages. Existing deep learning approaches require large longitudinal datasets and often fail to model the temporal continuity and modality irregularities inherent in real-world clinical data. To address these limitations, we propose the Diffusion-Guided Attention Network (DiGAN), which integrates latent diffusion modelling with an attention-guided convolutional network. The diffusion model synthesizes realistic longitudinal neuroimaging trajectories from limited training data, enriching temporal context and improving robustness to unevenly spaced visits. The attention-convolutional layer then captures discriminative structural-temporal patterns that distinguish cognitively normal subjects from those with mild cognitive impairment and subjective cognitive decline. Experiments on the ADNI dataset demonstrate that DiGAN outperforms existing state-of-the-art baselines, showing its potential for early-stage AD detection.

Problem

• Alzheimer's disease
• early diagnosis
• temporal irregularity
• structural brain changes
• longitudinal data

Innovation

• Diffusion Model
• Attention Mechanism
• Longitudinal Neuroimaging
• Early Alzheimer's Detection
• Temporal Modeling