Aiding Medical Diagnosis Through the Application of Graph Neural Networks to Functional MRI Scans
This work addresses the challenge of enhancing clinical diagnosis through neuroimaging data, but it appears incremental as it builds on existing GNN methods for a specific domain.
The paper tackled the problem of improving predictive accuracy for medical diagnosis by applying Graph Neural Networks (GNNs) to functional MRI scans, resulting in successful prediction of disease and sex without omitting voxels.
Graph Neural Networks (GNNs) have been shown to be a powerful tool for generating predictions from biological data. Their application to neuroimaging data such as functional magnetic resonance imaging (fMRI) scans has been limited. However, applying GNNs to fMRI scans may substantially improve predictive accuracy and could be used to inform clinical diagnosis in the future. In this paper, we present a novel approach to representing resting-state fMRI data as a graph containing nodes and edges without omitting any of the voxels and thus reducing information loss. We compare multiple GNN architectures and show that they can successfully predict the disease and sex of a person. We hope to provide a basis for future work to exploit the power of GNNs when applied to brain imaging data.