Investigation of domain specific pretraining of a Swin Transformer to improve Alzheimer's disease classification on three different brain imaging modalities

Early prediction of Alzheimer’s Disease (AD) is crucial for optimal patient care. It can be achieved by the inspection of suitable imaging modalities of the brain, namely structural T1-weighted MRI (T1w), Fludeoxyglucose-18 Positron Emission Tomography (FDG-PET), and Arterial Spin Labeling (ASL). In this work, we present image-based AD classification using a Swin Transformer model, and investigate the effect of domain specific pretraining utilizing Masked Image Modeling. The model was trained to predict the three classes cognitive normal (CN), mild cognitive impairment (MCI), and AD using T1w, FDG-PET, and ASL images retrieved from the ADNI database. Our results demonstrate the pretraining’s positive effect on the classification metrics for all modalities, reaching 92.9%. 90.3% and 82.7% ROC-AUC. They are competitive in comparison to reported state-of-the art approaches, in particular on the non-invasively retrieved ASL data