The Relevance Voxel Machine (RVoxM): a Bayesian Method for Image-based Prediction

Overview

Journal Med Image Comput Comput Assist Interv

Publisher Springer

Date 2011 Oct 19

PMID 22003689

Citations 12

Authors

Mert R Sabuncu

Koen Van Leemput

Affiliations

Soon will be listed here.

Abstract

This paper presents the Relevance Voxel Machine (RVoxM), a Bayesian multivariate pattern analysis (MVPA) algorithm that is specifically designed for making predictions based on image data. In contrast to generic MVPA algorithms that have often been used for this purpose, the method is designed to utilize a small number of spatially clustered sets of voxels that are particularly suited for clinical interpretation. RVoxM automatically tunes all its free parameters during the training phase, and offers the additional advantage of producing probabilistic prediction outcomes. Experiments on age prediction from structural brain MRI indicate that RVoxM yields biologically meaningful models that provide excellent predictive accuracy.

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Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

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Addressing Confounding in Predictive Models with an Application to Neuroimaging.

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