ArticleIEEE transactions on medical imaging2012
JointMMCC: joint maximum-margin classification and clustering of imaging data.
Article in IEEE transactions on medical imaging, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2015Pooled it
- Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.Schizophrenia bulletin · 2025Review
- Article
- Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review.Frontiers in molecular neuroscience · 2022Review
- Multi-scale semi-supervised clustering of brain images: Deriving disease subtypes.Medical image analysis · 2022Article
- Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning.Frontiers in applied mathematics and statistics · 2018Article
- Consensus clustering approach to group brain connectivity matrices.Network neuroscience (Cambridge, Mass.) · 2017Article
- HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework.NeuroImage · 2017Article
- Beyond Lumping and Splitting: A Review of Computational Approaches for Stratifying Psychiatric Disorders.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2016Review
- Capturing heterogeneous group differences using mixture-of-experts: Application to a study of aging.NeuroImage · 2016Article
- Disentangling Disease Heterogeneity with Max-Margin Multiple Hyperplane Classifier.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2015Article
- Neural signatures of autism spectrum disorders: insights into brain network dynamics.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2015Review
- Heterogeneity of structural brain changes in subtypes of schizophrenia revealed using magnetic resonance imaging pattern analysis.Schizophrenia bulletin · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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.
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