COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements

By Yong Fan, Dinggang Shen, Ruben C. Gur, Raquel E. Gur, Christos Davatzikos. 2007

390 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Geometry and Topology

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Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, positive): “Machine learning based methods Davatzikos (2004) Cuingnet et al. (2011) De Martino et al. (2008) Fan et al. (2007) Gaonkar and Davatzikos (2012) Xu et al. (2009) Van De Ville et al. (2007) Van De Ville et al. (2004) Mingoia et al. (2012) can offer diagnostics based on neuroimaging data.”
DRAMMS: Deformable Registration via Attribute Matching and Mutual-Saliency Weighting (2009, methodology): “…and similarity of the matching on those selected training voxel pairs, max In implementation, DRAMMS adopts an iterative backward elimination and forward inclusion strategy for attribute selection, which are commonly used for attribute/variable selection in the machine learning community [29].”
A Review of Feature Reduction Techniques in Neuroimaging (2013, background): “2011) and in gender (Men vs Women) classification (Fan et al. 2006).”
Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI (2008, neutral): “This approach considers all brain regions jointly, and identi ﬁ es a minimal set of brain regions/clusters whose volumes jointly maximally differentiate between the two groups under consideration, on an individual scan basis; a detailed description can be found in (Fan et al., 2007a).”
Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database (2011, methodology): “We used a linear C-SVM for all the approaches except COMPARE (Fan et al., 2007) for which a non-linear C-SVM with a Gaussian kernel was used.”
A review on neuroimaging‐based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages (2017, neutral): “calculated RAVENS density maps and used a watershed clustering algorithm-based method (Fan et al., 2007) to calculate an adaptive set of ROIs for SVM-based AD classification, using the multi-centric ADNI (Alzheimer's disease neuroimaging initiative (Weiner et al.”
Generative-Discriminative Basis Learning for Medical Imaging (2012, methodology): “TABLE II COMPARISON OF THE PROPOSED METHOD WITH TWO DIFFERENT CONSTRAINTS BOXED- AND GROUP- WITH OTHER METHODS: SVD, NMF, AND [14].”
Alzheimer's disease diagnostics by adaptation of 3D convolutional network (2016, methodology): “The brain volume in [16] is segmented to GM, white matter (WM), and CSF parts, followed by cal-”
Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects (2014, neutral): “If ouraimwouldbetocapturethefullextentofatrophyinAD,amorespe-cialized feature selection method would probably be more adequate (Fan et al., 2007; Cuingnet et al., 2013; Grosenick et al., 2013; Michel et al., 2011).”
Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome (2011, methodology): “Theses approaches allow capturing complex multivariate relationships in the data and have been successfully applied to the individual classification of a variety of neurological conditions (Lao et al., 2004; Fan et al., 2007; Klöppel et al., 2008a; Vemuri et al., 2008).”
Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI (2010, methodology): “used a high-dimensional pattern classifier based on SVM and feature extraction to determine subtle and spatially complex structural patterns that distinguished schizophrenia patients from healthy controls (Fan et al., 2007).”
Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer’s Disease Diagnosis using Structural MRI (2018, methodology): “[12] adopted the”
Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification (2013, neutral): “This has lead to the development of multi-variate image analysis methods, which provide a more complete picture of imaging patterns that relate to brain activity, structure and pathology (Craddock et al., 2009; Cuingnet et al., 2011; Davatzikos et al., 2005; De Martino et al., 2008; Fan et al., 2007”
Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study (2008, methodology): “…Golland et al., 2002; Lao et al., 2004; Liu et al., 2004; Timoner et al., 2002), and have shown great potential in a variety of neuroimaging studies (Davatzikos et al., 2008, 2005a,b; Fan et al., 2008a, 2005, 2006b, 2007b, Kawasaki et al., 2007; Mourao-Miranda et al., 2005; Yoon et al., 2007).”
Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging (2009, neutral): “…the use of multidimensional classification methods, such as support vector machines (SVM) (Burges, 1998; Cristianini and ShaweTaylor, 2000), to assist in the diagnosis of neurological and psychiatric pathologies (Fan et al., 2007, 2008; Kloppel et al., 2008; Lao et al., 2004; Vemuri et al., 2008).”