Semi-supervised Pattern Classification: Application to Structural MRI of Alzheimer's Disease

• Published in: 2011 International Workshop on Pattern Recognition in Neuroimaging Vol. 2011; pp. 1 - 4
• Main Authors: Dong Hye Ye, Pohl, K. M., Davatzikos, C.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.05.2011
• Subjects: Accuracy; Alzheimer's disease; Biomedical imaging; Early detection; Feature extraction; Manifold learning; Manifolds; Mild cognitive impairment; Semi-supervised; Support vector machines; Manifold learning; Alzheimer’s disease; Mild cognitive impairment; Early detection; Semi-supervised
• ISBN: 9781457701115; 1457701111
• ISSN: 2330-9989
• DOI: 10.1109/PRNI.2011.12

This paper presents an image-based classification method, and applies it to classification of brain MRI scans of individuals with Mild Cognitive Impairment (MCI). The high dimensionality of the image data is reduced using nonlinear manifold learning techniques, thereby yielding a low-dimensional embedding. Features of the embedding are used in conjunction with a semi-supervised classifier, which utilizes both labeled and unlabeled images to boost performance. The method is applied to 237 scans of MCI patients in order to predict conversion from MCI to Alzheimer's Disease. Experimental results demonstrate better prediction accuracy compared to a state-of-the-art method.