Developing a Dynamic Graph Network for Interpretable Analysis of Multi-Modal MRI Data in Parkinson’s Disease Diagnosis

TL;DR: An Interpretable Graph-Learning Convolutional Network (iGLCN) is proposed to enhance the performance of personalized diagnosis for PD while simultaneously producing interpretable results, and can interpret diagnosis outcomes.

Abstract: Following the aging of the population, Parkinson's disease (PD) poses a severe challenge to public health. For the diagnosis of PD and the prediction of its progression, numerous computer-aided diagnosis procedures have been developed. Recently, Graph Convolutional Networks (GCN) are widely applied in deep learning to effectively integrate multi-modal features and model subject correlation. However, many GCNs which are used for node classification build large-scale fixed graph topologies using the entire dataset, which could make them impossible to verify independently. Furthermore, past GCN algorithms would need more interpretability, limiting their real-world applications. In this paper, an Interpretable Graph-Learning Convolutional Network (iGLCN) is proposed to enhance the performance of personalized diagnosis for PD while simultaneously producing interpretable results. The proposed method can dynamically adjust the graph structure for GCN to better diagnose outcomes by learning the optimal underlying latent graph. Through interpretable feature learning, the proposed network can interpret diagnosis outcomes. The experiments showed that the proposed method increased flexibility while maintaining a high level of classification performance and could be interpretable for PD diagnosis.Clinical Relevance— The proposed method is expected to have good performance in its strong practicability, feasibility, and interpretability for Parkinson’s disease diagnosis.