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IEEE Trans Med Imaging. 2012 Jan;31(1):51-69. doi: 10.1109/TMI.2011.2162961. Epub 2011 Jul 25.

Generative-discriminative basis learning for medical imaging.

Author information

1
Department of Electrical and System Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA. batmanghelich@gmail.com

Abstract

This paper presents a novel dimensionality reduction method for classification in medical imaging. The goal is to transform very high-dimensional input (typically, millions of voxels) to a low-dimensional representation (small number of constructed features) that preserves discriminative signal and is clinically interpretable. We formulate the task as a constrained optimization problem that combines generative and discriminative objectives and show how to extend it to the semi-supervised learning (SSL) setting. We propose a novel large-scale algorithm to solve the resulting optimization problem. In the fully supervised case, we demonstrate accuracy rates that are better than or comparable to state-of-the-art algorithms on several datasets while producing a representation of the group difference that is consistent with prior clinical reports. Effectiveness of the proposed algorithm for SSL is evaluated with both benchmark and medical imaging datasets. In the benchmark datasets, the results are better than or comparable to the state-of-the-art methods for SSL. For evaluation of the SSL setting in medical datasets, we use images of subjects with mild cognitive impairment (MCI), which is believed to be a precursor to Alzheimer's disease (AD), as unlabeled data. AD subjects and normal control (NC) subjects are used as labeled data, and we try to predict conversion from MCI to AD on follow-up. The semi-supervised extension of this method not only improves the generalization accuracy for the labeled data (AD/NC) slightly but is also able to predict subjects which are likely to converge to AD.

PMID:
21791408
PMCID:
PMC3402718
DOI:
10.1109/TMI.2011.2162961
[Indexed for MEDLINE]
Free PMC Article

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