Paris Dauphine University

About: Paris Dauphine University is a education organization based out in Paris, France. It is known for research contribution in the topics: Context (language use) & Population. The organization has 1766 authors who have published 6909 publications receiving 162747 citations. The organization is also known as: Paris Dauphine & Dauphine.

3D Multi-branch Tubular Surface and Centerline Extraction with 4D Iterative Key Points

Abstract: An innovative 3D multi-branch tubular structure and centerline extraction method is proposed in this paper In contrast to classical minimal path techniques that can only detect a single curve between two pre-defined initial points, this method propagates outward from only one initial seed point to detect 3D multi-branch tubular surfaces and centerlines simultaneously First, instead of only representing the trajectory of a tubular structure as a 3D curve, the surface of the entire structure is represented as a 4D curve along which every point represents a 3D sphere inside the tubular structure Then, from any given sphere inside the tubular structure, a novel 4D iterative key point searching scheme is applied, in which the minimal action map and the Euclidean length map are calculated with a 4D freezing fast marching evolution A set of 4D key points is obtained during the front propagation process Finally, by sliding back from each key point to the previous one via the minimal action map until all the key points are visited, we are able to fully obtain global minimizing multi-branch tubular surfaces An additional immediate benefit of this method is a natural notion of a multi-branch tube's "central curve" by taking only the first three spatial coordinates of the detected 4D multi-branch curve Experimental results on 2D/3D medical vascular images illustrate the benefits of this method

Bayesian Optimal Adaptive Estimation Using a Sieve Prior.

Abstract: . We derive rates of contraction of posterior distributions on non-parametric models resulting from sieve priors. The aim of the study was to provide general conditions to get posterior rates when the parameter space has a general structure, and rate adaptation when the parameter is, for example, a Sobolev class. The conditions employed, although standard in the literature, are combined in a different way. The results are applied to density, regression, nonlinear autoregression and Gaussian white noise models. In the latter we have also considered a loss function which is different from the usual l2 norm, namely the pointwise loss. In this case it is possible to prove that the adaptive Bayesian approach for the l2 loss is strongly suboptimal and we provide a lower bound on the rate.

Adaptive lasso and group-lasso for functional Poisson regression

Abstract: High dimensional Poisson regression has become a standard framework for the analysis of massive counts datasets. In this work we estimate the intensity function of the Poisson regression model by using a dictionary approach, which generalizes the classical basis approach, combined with a Lasso or a group-Lasso procedure. Selection depends on penalty weights that need to be calibrated. Standard methodologies developed in the Gaussian framework can not be directly applied to Poisson models due to heteroscedasticity. Here we provide data-driven weights for the Lasso and the group-Lasso derived from concentration inequalities adapted to the Poisson case. We show that the associated Lasso and group-Lasso procedures satisfy fast and slow oracle inequalities. Simulations are used to assess the empirical performance of our procedure, and an original application to the analysis of Next Generation Sequencing data is provided.