Abstract / truncated to 115 words (read the full abstract)

In this thesis, the power of Machine Learning (ML) algorithms is combined with brain connectivity patterns, using Magnetic Resonance Imaging (MRI), for classification and prediction of Multiple Sclerosis (MS). White Matter (WM) as well as Grey Matter (GM) graphs are studied as connectome data types. The thesis addresses three main research objectives. The first objective aims to generate realistic brain connectomes data for improving the classification of MS clinical profiles in cases of data scarcity and class imbalance. To solve the problem of limited and imbalanced data, a Generative Adversarial Network (GAN) was developed for the generation of realistic and biologically meaningful connec- tomes. This network achieved a 10% better MS classification performance compared to ... toggle 2 keywords

multiple sclerosis mri brain

Information

Author
Barile, Berardino
Institution
KU Leuven
Supervisors
Publication Year
2022
Upload Date
Feb. 13, 2023

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