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Learning From Crowds
For many supervised learning tasks it may be infeasible (or very expensive) to obtain objective and reliable labels. Instead, we can collect subjective (possibly noisy) labels from multiple expertsExpand
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Variational Methods for Multimodal Image Matching
Matching images of different modalities can be achieved by the maximization of suitable statistical similarity measures within a given class of geometric transformations. Handling complex, nonrigidExpand
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Supervised learning from multiple experts: whom to trust when everyone lies a bit
We describe a probabilistic approach for supervised learning when we have multiple experts/annotators providing (possibly noisy) labels but no absolute gold standard. The proposed algorithm evaluatesExpand
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Modeling annotator expertise: Learning when everybody knows a bit of something
Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this problem whenExpand
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Flows of diffeomorphisms for multimodal image registration
We present a theoretical and computational framework for nonrigid multimodal registration. We proceed by minimization of statistical similarity criteria (global and local) in a variational framework,Expand
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Whole-Body PET/MR Imaging: Quantitative Evaluation of a Novel Model-Based MR Attenuation Correction Method Including Bone
In routine whole-body PET/MR hybrid imaging, attenuation correction (AC) is usually performed by segmentation methods based on a Dixon MR sequence providing up to 4 different tissue classes. BecauseExpand
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Dixon Sequence with Superimposed Model-Based Bone Compartment Provides Highly Accurate PET/MR Attenuation Correction of the Brain
Simultaneous PET/MR of the brain is a promising technology for characterizing patients with suspected cognitive impairment or epilepsy. Unlike CT, however, MR signal intensities do not correlateExpand
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How to deal with point correspondences and tangential velocities in the level set framework
In this paper, we overcome a major drawback of the level set framework: the lack of point correspondences. We maintain explicit backward correspondences from the evolving interface to the initial oneExpand
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Dense image matching with global and local statistical criteria: a variational approach
We present two novel algorithms for multimodal, dense matching of two images using a variational approach. These algorithms complete and generalise our previous work by treating the case ofExpand
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The Inverse EEG and MEG Problems : The Adjoint State Approach I: The Continuous Case
In this report, we study the problem of the three-dimensional reconstruction of the electrical activity of the brain from electroencephalography (EEG) and magnetoencephalography (MEG). We use aExpand
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