Disease classification and prediction via semi-supervised dimensionality reduction

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 dec...

Full description

Saved in:
Bibliographic Details
Published in:2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Vol. 2011; pp. 1086 - 1090
Main Authors: Batmanghelich, K N, Ye, D H, Pohl, K M, Taskar, B, Davatzikos, C
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01.03.2011
Subjects:
Accuracy
Alzheimer's disease
Basis Learning
Biomedical imaging
Laplace equations
Matrix decomposition
Matrix factorization
Mild Cognitive Impairment (MCI)
Optimization
Semi-supervised Learning
Basis Learning
Mild Cognitive Impairment (MCI)
Semi-supervised Learning
Matrix factorization
Alzheimer’s disease
Optimization
ISBN:1424441277, 9781424441273
ISSN:1945-7928, 1945-8452
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Data used in the preparation of this article were obtained from the Alzheimer Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI)
ISBN:1424441277
9781424441273
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2011.5872590