Generative-Discriminative Basis Learning for Medical Imaging
By Nematollah Kayhan Batmanghelich, B. Taskar, Christos Davatzikos. 2012
76 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Vince D. Calhoun
- Jing Sui
- Alberto Domingo Pascual-Montano
- Javier Vargas
- Christos Davatzikos
- Sergey M. Plis
- Aristeidis Sotiras
- Carlos Oscar Sánchez Sorzano
In Computer Vision and Pattern Recognition
- Aristeidis Sotiras
- Vince D. Calhoun
- Jing Sui
- Sergey M. Plis
- Mohammad R. Arbabshirani
- Christos Davatzikos
- Alberto Domingo Pascual-Montano
- Javier Vargas
Citations in the corpus
- Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, neutral): “…have been widely used in the neuroimaging community to generate diagnostic scores on the basis of brain images Mouro-Miranda et al. (2005) Wang et al. (2007) Cuingnet et al. (2011) Craddock et al. (2009) Fan et al. (2007) Batmanghelich et al. (2012) Vemuri et al. (2008) Davatzikos et al. (2011).”
- Single Subject Prediction of Brain Disorders in Neuroimaging: Promises and Pitfalls (2016, methodology): “t2:33 sMRI AD Pdf of VOI based on VBM 100 SVM HC = 130, AD = 130, Total = 260 86% Beheshti and Demirel (2015) t2:34 sMRI AD Generative-discriminative basis vectors based on RAVEN maps 30–50 Logistic model trees HC = 63, AD = 54, Total = 117 87–89% Batmanghelich et al. (2012)”
- Generative Discriminative Models for Multivariate Inference and Statistical Mapping in Medical Imaging (2018, background): “Hybrid generative discriminative models have been proposed to improve the interpretability of discriminative models [11, 2].”
- Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification (2013, unknown): “For instance, methods described in Batmanghelich et al. (2012), Gaonkar et al. (2011), Ryali et al. (2010) and Sabuncu and Van Leemput (2011)) attempt to apply sparsity to make interpretations/inferences.”
- A survey of dimensionality reduction techniques (2014, background): “Many other works followed since then, for the most recent works and for a bibliographical review see [Bian2011, Cai2011, Kim2011, Lin2011, Batmanghelich2012].”