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Project

Machine Learning Methods for Multiple Sclerosis Classification and Prediction using MRI Brain Connectivity

The main objective of this project is to analyze Multiple Sclerosis patients by means of Deep Neural Networks and more generally by Machine Learning models. These fully automated methods can be exploited for both structural and functional information retrieved by Magnetic Resonance Imaging (MRI) techniques. In more detail, MS patients are mainly characterized by inflammatory cascades and/or neurodegenerative mechanisms, or local inflammatory and/or demyelinating lesions. The causes of such degenerations are still unknown and unpredictable. In this thesis, automatic methods both supervised and unsupervised will be exploited for the classification and prognosis of MS patients by means of MRI images and graph theory.

Date:25 Jan 2021 →  6 Oct 2022
Keywords:Machine Learning, Deep Learning, Artificial Intelligence, Brain Imaging, Brain Connectome, Magnetic Resonance Imaging
Disciplines:Artificial intelligence not elsewhere classified
Project type:PhD project