Aux-ViT : Classification of Alzheimer's Disease from MRI based on Vision Transformer with Auxiliary Branch

Alzheimer's disease (AD) has become a major public health concern, and reliable screening and diagnosis remain difficult. Magnetic resonance image (MRI) can be helpful in distinguishing AD patients from individuals with normal cognition (NC). Deep neural networks have demonstrated strong capacities for extracting intricate nonlinear correlations from brain imaging data recently. However, this requires large amounts of data for training to avoid overfitting problems, but data is scarce and precious in medical field. In this work, we propose a Vision Transformer network architecture called Aux-ViT, which solves the problem of losing shallow features by adding a class auxiliary branch. Specifically, we choose ViT as the backbone network and add an Auxiliary Multi-layer Perceptron Head to output auxiliary prediction results for calculating prediction errors. Based on the characteristics of MRI, we also developed a brain MRI data preprocessing method called Multi-Information Fusion Improvement, while further achieving data enhancement using a random synthetic mask based on pixel weighting fusion. We conducted extensive experiments using the ADNI-3 dataset to validate our algorithm. When compared to the baseline ViT model, the Aux-ViT model obtains an accuracy of 89.58%, which is an increase in accuracy of 3.93% and a decrease in training time of 47.7%. Our study provides a practical approach for early Alzheimer's diagnostics utilizing MRI data.