Graph based neural networks for automatic classification of multiple sclerosis clinical courses

Automatic classification of biomedical imaging became an important field of research within the scientific community, in the latest years. Indeed, advances in image acquisition and processing techniques, along with the success of novel deep learning methods and architectures, represented a considerable support in providing better biomarkers for the characterization of several diseases, and brain diseases in particular. In this work we propose a novel neural network approach that is applied to graphs generated from MRI data in order to make predictions about the clinical status of a patient. Results show high performances in classification tasks and open interesting perspectives in the field.