Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease
This work addresses the need for improved diagnostic tools in Parkinson's Disease, a prevalent neurodegenerative condition, though it appears incremental as it applies an existing deep learning method to a specific medical imaging task.
The paper tackled the problem of distinguishing Parkinson's Disease cases from controls by fusing multiple brain image modalities using a Graph Convolutional Network, achieving an AUC of 0.9537±0.0587 compared to 0.6443±0.0223 for traditional methods.
Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans. PD is highly progressive and heterogeneous. Quite a few studies have been conducted in recent years on predictive or disease progression modeling of PD using clinical and biomarkers data. Neuroimaging, as another important information source for neurodegenerative disease, has also arisen considerable interests from the PD community. In this paper, we propose a deep learning method based on Graph Convolutional Networks (GCN) for fusing multiple modalities of brain images in relationship prediction which is useful for distinguishing PD cases from controls. On Parkinson's Progression Markers Initiative (PPMI) cohort, our approach achieved $0.9537\pm 0.0587$ AUC, compared with $0.6443\pm 0.0223$ AUC achieved by traditional approaches such as PCA.