Title :
Disease classification and prediction via semi-supervised dimensionality reduction
Author :
Batmanghelich, K.N. ; Ye, D.H. ; Pohl, K.M. ; Taskar, Ben ; Davatzikos, Christos
Author_Institution :
Dept. of Radiol., Univ. of Pennsylvania, Philadelphia, PA, USA
fDate :
March 30 2011-April 2 2011
Abstract :
We present a new semi-supervised algorithm for 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 to semi-supervised learning. In addition, we add a new regularization term to the objective function to better captur 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 diagnosed with 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 with MCI 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.
Keywords :
diseases; image classification; learning (artificial intelligence); medical image processing; constrained matrix decomposition; disease prediction; image-based disease classification; medical imaging; mild cognitive impairment; semisupervised algorithm; semisupervised dimensionality reduction; semisupervised learning; Accuracy; Alzheimer´s disease; Biomedical imaging; Laplace equations; Matrix decomposition; Optimization; Alzheimer´s disease; Basis Learning; Matrix factorization; Mild Cognitive Impairment (MCI); Optimization; Semi-supervised Learning;
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on
Conference_Location :
Chicago, IL
Print_ISBN :
978-1-4244-4127-3
Electronic_ISBN :
1945-7928
DOI :
10.1109/ISBI.2011.5872590