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

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...

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Bibliographic Details
Published in2014 International Workshop on Pattern Recognition in Neuroimaging pp. 1 - 4
Main Authors Moradi, Elaheh, Tohka, Jussi, Gaser, Christian
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2014
Subjects
Alzheimer's disease
Magnetic resonance imaging
Neuroimaging
Semisupervised learning
Support vector machines
Training
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Summary: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 data - in this case data from MCI subjects whose final diagnosis is not yet known. We compare two widely used semi-supervised methods (low density separation (LDS) and semi-supervised discriminant analysis (SDA)) to the corresponding supervised methods using real and synthetic MRI data of MCI subjects. With simulated data, using SSL instead of supervised learning led to higher classification performance in certain cases, however, the applicability of semi-supervised methods depended strongly on the data distributions. With real MRI data, the SSL methods achieved significantly better classification performances over supervised methods. Moreover, even using a small number of unlabeled samples improved the AD conversion predictions.
DOI:10.1109/PRNI.2014.6858535