Deriving statistical significance maps for SVM based image classification and group comparisons

By Bilwaj Gaonkar, Christos Davatzikos. 2012

26 citations recorded by the source corpus.

Population based pattern analysis and classification for quantifying structural and functional differences between diverse groups has been shown to be a powerful tool for the study of a number of diseases, and is quite commonly used especially in neuroimaging. The alternative to these pattern analysis methods, namely mass univariate methods such as voxel based analysis and all related methods, cannot detect multivariate patterns associated with group differences, and are not particularly suitable for developing individual-based diagnostic and prognostic biomarkers. A commonly used pattern analysis tool is the support vector machine (SVM). Unlike univariate statistical frameworks for morphometry, analytical tools for statistical inference are unavailable for the SVM. In this paper, we show that null distributions ordinarily obtained by permutation tests using SVMs can be analytically approximated from the data. The analytical computation takes a small fraction of the time it takes to do an actual permutation test, thereby rendering it possible to quickly create statistical significance maps derived from SVMs. Such maps are critical for understanding imaging patterns of group differences and interpreting which anatomical regions are important in determining the classifier's decision.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, neutral): “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.”
Relevant Feature Set Estimation with a Knock-out Strategy and Random Forests (2015, unknown): “…(Mourao-Miranda et al., 2005; Kriegeskorte et al., 2006; Menze et al., 2009; Sabuncu and Van Leemput, 2012; Langs et al., 2011; Yamashita et al., 2008; Gaonkar and Davatzikos, 2012; Rondina et al., 2014) (We refer the reader to the recent review (Mwangi et al., 2014) for a more complete list.).”
Searchlight analysis: Promise, pitfalls, and potential (2013, methodology): “Permutation-based tests have also been proposed (Kriegeskorte et al., 2006), with new techniques increasing their interpretability and computational tractability (Gaonkar and Davatzikos, 2012; Stelzer et al., 2013).”
Identifying Predictive Regions from fMRI with TV-L1 Prior (2013, methodology): “For this, certain decoding procedures [2], [3] output a statistical test per voxel, a multivariate extension of standard analysis.”
Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification (2013, unknown): “Some of this work is based on concepts first presented by us in Gaonkar and Davatzikos (2012).”
Faster permutation inference in brain imaging (2016, background): “Forinfer-ence for support-vector machine problems, Gaonkar and Davatzikos (2012) suggested an analytical approximation to the permutation distribution of each component of the hyperplane that separates classes.”