Ensemble learning incorporating uncertain registration
Simpson IJA, Woolrich MW, Andersson JLR, Groves AR, Schnabel JA (2013)
Publication Type: Journal article
Publication year: 2013
Journal
Book Volume: 32
Pages Range: 748-756
Article Number: 6395830
Journal Issue: 4
Abstract
This paper proposes a novel approach for improving the accuracy of statistical prediction methods in spatially normalized analysis. This is achieved by incorporating registration uncertainty into an ensemble learning scheme. A probabilistic registration method is used to estimate a distribution of probable mappings between subject and atlas space. This allows the estimation of the distribution of spatially normalized feature data, e.g., grey matter probability maps. From this distribution, samples are drawn for use as training examples. This allows the creation of multiple predictors, which are subsequently combined using an ensemble learning approach. Furthermore, extra testing samples can be generated to measure the uncertainty of prediction. This is applied to separating subjects with Alzheimer's disease from normal controls using a linear support vector machine on a region of interest in magnetic resonance images of the brain. We show that our proposed method leads to an improvement in discrimination using voxel-based morphometry and deformation tensor-based morphometry over bootstrap aggregating, a common ensemble learning framework. The proposed approach also generates more reasonable soft-classification predictions than bootstrap aggregating. We expect that this approach could be applied to other statistical prediction tasks where registration is important. © 1982-2012 IEEE.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Simpson, I.J.A., Woolrich, M.W., Andersson, J.L.R., Groves, A.R., & Schnabel, J.A. (2013). Ensemble learning incorporating uncertain registration. IEEE Transactions on Medical Imaging, 32(4), 748-756. https://doi.org/10.1109/TMI.2012.2236651
MLA:
Simpson, Ivor J. A., et al. "Ensemble learning incorporating uncertain registration." IEEE Transactions on Medical Imaging 32.4 (2013): 748-756.
BibTeX: Download