@inbook{8ca3a6f9886242b38fb83d5d3ad807cd,
title = "Bayesian model selection for pathological data",
abstract = "The detection of abnormal intensities in brain images caused by the presence of pathologies is currently under great scrutiny. Selecting appropriate models for pathological data is of critical importance for an unbiased and biologically plausible model fit, which in itself enables a better understanding of the underlying data and biological processes. Besides, it impacts on one's ability to extract pathologically meaningful imaging biomarkers. With this aim in mind, this work proposes a fully unsupervised hierarchical model selection framework for neuroimaging data which permits the stratification of different types of abnormal image patterns without prior knowledge about the subject's pathological status.",
author = "Sudre, {Carole H.} and Cardoso, {Manuel Jorge} and Willem Bouvy and Biessels, {Geert Jan} and Josephine Barnes and S{\'e}bastien Ourselin",
year = "2014",
month = jan,
day = "1",
doi = "10.1007/978-3-319-10404-1_41",
language = "English",
isbn = "9783319104034",
volume = "8673 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 1",
pages = "323--330",
booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings",
address = "Germany",
edition = "PART 1",
note = "17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 ; Conference date: 14-09-2014 Through 18-09-2014",
}