TY - GEN
T1 - Detecting outlying subjects in high-dimensional neuroimaging datasets with regularized minimum covariance determinant
AU - Fritsch, Virgile
AU - Varoquaux, Gael
AU - Thyreau, Benjamin
AU - Poline, Jean Baptiste
AU - Thirion, Bertrand
PY - 2011
Y1 - 2011
N2 - Medical imaging datasets used in clinical studies or basic research often comprise highly variable multi-subject data. Statistically-controlled inclusion of a subject in a group study, i.e. deciding whether its images should be considered as samples from a given population or whether they should be rejected as outlier data, is a challenging issue. While the informal approaches often used do not provide any statistical assessment that a given dataset is indeed an outlier, traditional statistical procedures are not well-suited to the noisy, high-dimensional, settings encountered in medical imaging, e.g. with functional brain images. In this work, we modify the classical Minimum Covariance Determinant approach by adding a regularization term, that ensures that the estimation is well-posed in high-dimensional settings and in the presence of many outliers. We show on simulated and real data that outliers can be detected satisfactorily, even in situations where the number of dimensions of the data exceeds the number of observations.
AB - Medical imaging datasets used in clinical studies or basic research often comprise highly variable multi-subject data. Statistically-controlled inclusion of a subject in a group study, i.e. deciding whether its images should be considered as samples from a given population or whether they should be rejected as outlier data, is a challenging issue. While the informal approaches often used do not provide any statistical assessment that a given dataset is indeed an outlier, traditional statistical procedures are not well-suited to the noisy, high-dimensional, settings encountered in medical imaging, e.g. with functional brain images. In this work, we modify the classical Minimum Covariance Determinant approach by adding a regularization term, that ensures that the estimation is well-posed in high-dimensional settings and in the presence of many outliers. We show on simulated and real data that outliers can be detected satisfactorily, even in situations where the number of dimensions of the data exceeds the number of observations.
KW - Minimum Covariance Determinant
KW - Outlier detection
KW - fMRI
KW - neuroimaging
KW - regularization
KW - robust estimation
UR - http://www.scopus.com/inward/record.url?scp=82255164557&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-23626-6_33
DO - 10.1007/978-3-642-23626-6_33
M3 - Conference contribution
C2 - 22003708
AN - SCOPUS:82255164557
SN - 9783642236259
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 264
EP - 271
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings
PB - Springer Verlag
T2 - 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
Y2 - 18 September 2011 through 22 September 2011
ER -