Parkinson Disease classification Using Contrastive Graph Cross-View Learning with Multimodal Fusion of SPECT Images and Clinical Features

Parkinson's Disease (PD) is a neurodegenerative neurological disorder that impacts movement and afflicts over 10 million people worldwide. Previous researches have come up with deep learning models for predicting Parkinson's disease primarily using medical images and didn't leverage the manifold structure in the dataset. Our study introduces a multimodal approach with both image and non-image features with a contrastive cross-view graph fusion for Parkinson's disease classification. Specifically, we designed a multimodal co-attention module to integrate embeddings from two distinct graph views derived from low dimensional representation of images and clinical features, enabling the extraction of more stable and structured features from the multiview data. Additionally, we have devised a simplified fusion method utilizing a contrastive loss for positive and negative pairs, to enhance the model's overall cross-view fusion learning capabilities. In our experiments, the graph-view multimodal approach can achieve an accuracy rate of 91% and an AUC of 92.8% in five-fold cross-validation, and it also demonstrates superior predictive capabilities on non-image data as compared to methods that rely solely on machine learning methods.