Parkinson's Disease Classification with Self-supervised Learning and Attention Mechanism.

Parkinson's disease (PD) is a neurodegenerative geriatric disease commonly occurring in middle-aged and elderly adults. Since PD is irreversible and its treatment only slows down its rate of development, the early diagnosis by accurate prediction is of great significance to retard its deterioration. However, the existing computer-assisted diagnosis methods for PD have limitations in exploring the implicit and spatial information in the brain. In view of this limitation, a 3D network based on self-supervised learning strategy and attention mechanism is proposed for PD classification in this paper. The proposed method put the input of successive frames from the preprocessed magnetic resonance imaging (MRI) data into 3D ResNet18 with the classifier module for PD classification. Specifically, the attention mechanism is used to explore the discriminative features. Meanwhile, a self-supervised learning pretext task and a regression task are designed to assist in training and improve the robustness of the proposed model. We use a 5-fold cross-validation strategy to corroborate our method's effectiveness on the Parkinson's Progression Markers Initiative (PPMI) dataset. The experimental results indicate that our proposed method has achieved an accuracy of 87.50% for PD classification, which outperforms the most state-of-the-art deep learning methods.