Self-Supervised Pretext Tasks for Alzheimer's Disease Classification using 3D Convolutional Neural Networks on Large-Scale Synthetic Neuroimaging Dataset

TL;DR

This study explores self-supervised learning with 3D CNNs on synthetic neuroimaging data to classify Alzheimer's Disease, demonstrating that synthetic data can effectively train models comparable to real data.

Contribution

It introduces a multi-task self-supervised framework using synthetic MRI data for Alzheimer's classification, highlighting the potential of synthetic datasets for pretraining.

Findings

Synthetic data yields comparable classification performance to real data.

Multi-task pretext learning improves feature extraction.

Data augmentation enhances model accuracy.

Abstract

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.