  Structural magnetic resonance imaging (MRI) studies have shown that
Alzheimer's Disease (AD) induces both localised and widespread neural
degenerative changes throughout the brain. However, the absence of segmentation
that highlights brain degenerative changes presents unique challenges for
training CNN-based classifiers in a supervised fashion. In this work, we
evaluated several unsupervised methods to train a feature extractor for
downstream AD vs. CN classification. Using the 3D T1-weighted MRI data of
cognitive normal (CN) subjects from the synthetic neuroimaging LDM100K dataset,
lightweight 3D CNN-based models are trained for brain age prediction, brain
image rotation classification, brain image reconstruction and a multi-head task
combining all three tasks into one. Feature extractors trained on the LDM100K
synthetic dataset achieved similar performance compared to the same model using
real-world data. This supports the feasibility of utilising large-scale
synthetic data for pretext task training. All the training and testing splits
are performed on the subject-level to prevent data leakage issues. Alongside
the simple preprocessing steps, the random cropping data augmentation technique
shows consistent improvement across all experiments.
