The Relevance Voxel Machine (RVoxM): A Self-Tuning Bayesian Model for Informative Image-Based Prediction

Mert R. Sabuncu, Koen Van Leemput

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    This paper presents the relevance voxel machine (RVoxM), a dedicated Bayesian model for making predictions based on medical imaging data. In contrast to the generic machine learning 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. We demonstrate RVoxM as a regression model by predicting age from volumetric gray matter segmentations, and as a classification model by distinguishing patients with Alzheimer's disease from healthy controls using surface-based cortical thickness data. Our results indicate that RVoxM yields biologically meaningful models, while providing state-of-the-art predictive accuracy.
    Original languageEnglish
    JournalI E E E Transactions on Medical Imaging
    Volume31
    Issue number12
    Pages (from-to)2290-2306
    ISSN0278-0062
    DOIs
    Publication statusPublished - 2012

    Keywords

    • Image classification
    • Pattern recognition

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