Developing a Graph Convolutional Network of Differentiable Graph Module for Multi-Modal MRI Data: An Application to Parkinson's Disease

With the ageing of the population, Parkinson's disease (PD) has presented a severe challenge to public health. Here, a deep-learning framework named the AMDGM model was proposed to predict PD patients at an early stage. Firstly, multi-modal image-based models were respectively generated using the AMDGM model. Then, a weighted ensemble network was created as the final model. The proposed method achieved the best AUC performance of 0.872 in the testing cohort, better than others. And the proposed method can predict PD patients early to help clinical radiologists formulate more targeted treatments in the future.