TY - JOUR
T1 - JointMMCC
T2 - Joint maximum-margin classification and clustering of imaging data
AU - Filipovych, Roman
AU - Resnick, Susan M.
AU - Davatzikos, Christos
N1 - Funding Information:
Manuscript received November 23, 2011; revised January 13, 2012; accepted January 24, 2012. Date of publication February 06, 2012; date of current version May 02, 2012. This work was supported in part by the Intramural Research Program of the National Institutes of Health (NIH), National Institute on Aging (NIA), and R01-AG14971, N01-AG-3-2124, N01-AG-3-2124. Asterisk indicates corresponding author. *R. Filipovych is with the Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA (e-mail: roman.filipovych@uphs.upenn.edu).
PY - 2012
Y1 - 2012
N2 - A number of conditions are characterized by pathologies that form continuous or nearly-continuous spectra spanning from the absence of pathology to very pronounced pathological changes (e.g., normal aging, mild cognitive impairment, Alzheimer's). Moreover, diseases are often highly heterogeneous with a number of diagnostic subcategories or subconditions lying within the spectra (e.g., autism spectrum disorder, schizophrenia). Discovering coherent subpopulations of subjects within the spectrum of pathological changes may further our understanding of diseases, and potentially identify subconditions that require alternative or modified treatment options. In this paper, we propose an approach that aims at identifying coherent subpopulations with respect to the underlying MRI in the scenario where the condition is heterogeneous and pathological changes form a continuous spectrum. We describe a joint maximum-margin classification and clustering (JointMMCC) approach that jointly detects the pathologic population via semi-supervised classification, as well as disentangles heterogeneity of the pathological cohort by solving a clustering subproblem. We propose an efficient solution to the nonconvex optimization problem associated with JointMMCC. We apply our proposed approach to an medical resonance imaging study of aging, and identify coherent subpopulations (i.e., clusters) of cognitively less stable adults.
AB - A number of conditions are characterized by pathologies that form continuous or nearly-continuous spectra spanning from the absence of pathology to very pronounced pathological changes (e.g., normal aging, mild cognitive impairment, Alzheimer's). Moreover, diseases are often highly heterogeneous with a number of diagnostic subcategories or subconditions lying within the spectra (e.g., autism spectrum disorder, schizophrenia). Discovering coherent subpopulations of subjects within the spectrum of pathological changes may further our understanding of diseases, and potentially identify subconditions that require alternative or modified treatment options. In this paper, we propose an approach that aims at identifying coherent subpopulations with respect to the underlying MRI in the scenario where the condition is heterogeneous and pathological changes form a continuous spectrum. We describe a joint maximum-margin classification and clustering (JointMMCC) approach that jointly detects the pathologic population via semi-supervised classification, as well as disentangles heterogeneity of the pathological cohort by solving a clustering subproblem. We propose an efficient solution to the nonconvex optimization problem associated with JointMMCC. We apply our proposed approach to an medical resonance imaging study of aging, and identify coherent subpopulations (i.e., clusters) of cognitively less stable adults.
KW - Aging
KW - clustering
KW - magnetic resonance imaging (MRI)
KW - semi-supervised classification
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U2 - 10.1109/TMI.2012.2186977
DO - 10.1109/TMI.2012.2186977
M3 - Article
C2 - 22328179
AN - SCOPUS:84860652702
SN - 0278-0062
VL - 31
SP - 1124
EP - 1140
JO - IEEE transactions on medical imaging
JF - IEEE transactions on medical imaging
IS - 5
M1 - 6146434
ER -