Semi-supervised learning in MCI-to-ad conversion prediction - When is unlabeled data useful?

• Published in: 2014 International Workshop on Pattern Recognition in Neuroimaging pp. 1 - 4
• Main Authors: Moradi, Elaheh, Tohka, Jussi, Gaser, Christian
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2014
• Subjects: Alzheimer's disease; Magnetic resonance imaging; Neuroimaging; Semisupervised learning; Support vector machines; Training

This paper investigates the use of semi-supervised learning (SSL) for predicting Alzheimers Disease (AD) conversion in Mild Cognitive Impairment (MCI) patients based on Magnetic Resonance Imaging (MRI). SSL methods differ from standard supervised learning methods in that they make use of unlabeled d...