The Relevance Voxel Machine (RVoxM): a Bayesian method for image-based prediction. Academic Article uri icon

Overview

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.

publication date

  • January 1, 2011

Research

keywords

  • Brain
  • Brain Mapping
  • Image Processing, Computer-Assisted
  • Magnetic Resonance Imaging

Identity

PubMed Central ID

  • PMC3266486

Scopus Document Identifier

  • 82255181734

Digital Object Identifier (DOI)

  • 10.1007/978-3-642-23626-6_13

PubMed ID

  • 22003689

Additional Document Info

volume

  • 14

issue

  • Pt 3