Cart (Loading....) | Create Account
Close category search window
 

A fast mean-field method for large-scale high-dimensional data and its application in colonic polyp detection at CT colonography

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Shijun Wang ; Dept. of Radiol. & Imaging Sci., Nat. Institutes of Health, Bethesda, MD, USA ; Summers, R.M. ; Changshui Zhang

In this paper, we propose a fast mean-field method called LHMF to handle probabilistic models of large-scale data in high dimensional space. By using diffusion map locally linear embedding method which is a non-linear dimensionality reduction method, we first embed the high dimensional data into a low dimensional space. Then we construct a coarse-grained graph which preserves the spectral properties of original weighted graph in the high dimensional space by clustering. A new spin model is defined in the diffusion space and the geometric centroids of clusters represent variables in the new spin model. The calculation demand of mean-field methods can be reduced greatly on the coarse-grained spin model. The final marginal moments of original variables are derived from the states of geometric centroids by using geometric harmonics. We first tested the proposed method on the MNIST hand-written digits dataset. Experimental results show that the LHMF method is competent with consistency approach, a state-of-the-art semi-supervised learning method. Then we applied the proposed method to a large-scale colonic polyp dataset from computed tomography (CT) scans. Free-response operator characteristic analysis shows that our method achieves higher sensitivity with lower false positive rate compared with support vector machines.

Published in:

Neural Networks, 2009. IJCNN 2009. International Joint Conference on

Date of Conference:

14-19 June 2009

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.