The Relevance Voxel Machine (RVoxM): A Bayesian Method for Image-Based Prediction

Mert R. Sabuncu, Koen Van Leemput

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    This paper presents the Relevance VoxelMachine (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.
    Original languageEnglish
    Title of host publicationMedical Image Computing and Computer-Assisted Intervention –MICCAI2011 : 14th International Conference Toronto, Canada, September 18-22, 2011 Proceedings
    Volume3
    PublisherSpringer
    Publication date2011
    Pages99-106
    ISBN (Print)978-3-642-23625-9
    ISBN (Electronic)978-3-642-23626-6
    DOIs
    Publication statusPublished - 2011
    Event14th International Conference on Medical Image Computing and Computer-Assisted Intervention - Toronto, Canada
    Duration: 18 Sep 201122 Sep 2011
    Conference number: 14

    Conference

    Conference14th International Conference on Medical Image Computing and Computer-Assisted Intervention
    Number14
    CountryCanada
    CityToronto
    Period18/09/201122/09/2011
    SeriesLecture Notes in Computer Science
    Number6893
    ISSN0302-9743

    Keywords

    • MRI
    • Multivariate Pattern Analysis

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