MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
One-Class Novelty Detection for Seizure Analysis from Intracranial EEG
The Journal of Machine Learning Research
Joint Bayesian Cortical Sulci Recognition and Spatial Normalization
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
On the Manifold Structure of the Space of Brain Images
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Fast and Scalable Local Kernel Machines
The Journal of Machine Learning Research
Combining morphological information in a manifold learning framework: application to neonatal MRI
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
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Medical imaging datasets used in clinical studies or basic research often comprise highly variable multi-subject data. Statisticallycontrolled 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.