SMIL-DeiT:Multiple Instance Learning and Self-supervised Vision Transformer network for Early Alzheimer's disease classification

Abstract

Early diagnosis of Alzheimer's disease(AD) is becoming increasingly important in preventing and treating the disease as the world's population ages. We proposed a SMIL-DeiT network for AD classification tasks amongst three groups: Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Normal Cognitive (NC) in this study. Vision Transformer is the fundamental structure of our work. The data pre-training is performed utilizing DINO, a self-supervised technique, whereas the downstream classification task is done with Multiple Instance Learning. Our proposed technique works on the ADNI dataset. We used four performance metrics accuracy rates, precision, recall, and Fl-score in the evaluation, the most important of which was accuracy. The accuracy obtained by our method is higher than the transformer's 90.1% and CNN's 90.8%, reaching 93.2%.