The relevance voxel machine (RVoxM): A Bayesian method for image-based prediction

Authors:
Mert R. Sabuncu;Koen Van Leemput
Affiliations:
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, MGH, Harvard Medical School and Computer Science and Artificial Intelligence Laboratory, MIT;Athinoula A. Martinos Center for Biomed. Imaging, MGH, Harvard Med. Sch. and Comp. Sci. and Artificial Int. Lab., MIT and Depts. of Inf. and Comp. Sci. and of Biomed. Eng. and Computational Sci., ...
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Year:
2011

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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.