TY - JOUR
T1 - Generative-discriminative basis learning for medical imaging
AU - Batmanghelich, Nematollah K.
AU - Taskar, Ben
AU - Davatzikos, Christos
N1 - Funding Information:
Manuscript received May 20, 2011; accepted July 13, 2011. Date of publication July 25, 2011; date of current version December 30, 2011. This work was supported by the National Institutes of Health under Grant R01-AG-14971. Asterisk indicates corresponding author. *N. K. Batmanghelich is with the Department of Electrical and System Engineering, University of Pennsylvania, Philadelphia, PA 19104 USA (e-mail: batmanghelich@gmail.com). B. Taskar is with the Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104 USA. C. Davatzikos is with the Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TMI.2011.2162961
PY - 2012/1
Y1 - 2012/1
N2 - 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.
AB - 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.
KW - Basis learning
KW - classification
KW - feature construction
KW - generative-discriminative learning
KW - machine learning
KW - matrix factorization
KW - morphological pattern analysis
KW - optimization
KW - semi-supervised learning
KW - sparsity
UR - http://www.scopus.com/inward/record.url?scp=84855382390&partnerID=8YFLogxK
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U2 - 10.1109/TMI.2011.2162961
DO - 10.1109/TMI.2011.2162961
M3 - Article
C2 - 21791408
AN - SCOPUS:84855382390
SN - 0278-0062
VL - 31
SP - 51
EP - 69
JO - IEEE transactions on medical imaging
JF - IEEE transactions on medical imaging
IS - 1
M1 - 5961630
ER -