Disease Classification and Prediction via Semi-Supervised Dimensionality Reduction

N. Batmanghelich, D. Ye, K. Pohl, B. Taskar, C. Davatzikos, Disease Classification and Prediction via Semi-Supervised Dimensionality Reduction, ISBI 2011 (Oral Presentation)

We present a new semi-supervised algorithmfor dimensionality reduction which exploits information of unlabeled data in order to improve the accuracy of image-based disease classification based on medical images. We perform dimensionality reduction by adopting the formalism of constrained matrix decomposition of [1] to semi-supervised learning. In addition, we add a new regularization term to the objective function to better capture the affinity between labeled and unlabeled data. We apply our method to a data set consisting of medical scans of subjects classified as Normal Control (CN) and Alzheimer (AD). The unlabeled data are scans of subjects diagnosedwith Mild Cognitive Impairment (MCI), which are at high risk to develop AD in the future. We measure the accuracy of our algorithm in classifying scans as AD and NC. In addition, we use the classifier to predict which subjects withMCI will converge to AD and compare those results to the diagnosis given at later follow ups. The experiments highlight that unlabeled data greatly improves the accuracy of our classifier.