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Mélanges de lois de Student à Échelles Multiples pour la caractérisation de tumeurs par IRM multiparamétrique

Alexis Arnaud 1 Florence Forbes 1 Benjamin Lemasson 2 Emmanuel Barbier 2 Nicolas Coquery 2
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
2 Equipe 5 : NeuroImagerie Fonctionnelle et Perfusion Cérébrale
INSERM - Institut National de la Santé et de la Recherche Médicale : U1216, GIN - [GIN] Grenoble Institut des Neurosciences, UGA - Université Grenoble Alpes
Abstract : In this study we develop a statistical method for the classification of multiparametric MRI, which allows data quality control (atypical observation detection), and provides a tumour signatures dictionary. A previous study has been based on a Gaussian mixture model in which observations are grouped into classes corresponding to Gaussian distributions. This model is known for its sensitivity to outliers which can degrade the relevance of the obtained groups. As an alternative, we propose to use generalized Student distributions which extend the standard multivariate Student distribution by allowing different weights on the different dimensions of each observation.
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https://hal.archives-ouvertes.fr/hal-01253588
Contributor : Alexis Arnaud <>
Submitted on : Monday, January 11, 2016 - 9:43:21 AM
Last modification on : Thursday, May 6, 2021 - 3:07:48 AM
Long-term archiving on: : Tuesday, April 12, 2016 - 11:06:17 AM

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Alexis Arnaud, Florence Forbes, Benjamin Lemasson, Emmanuel Barbier, Nicolas Coquery. Mélanges de lois de Student à Échelles Multiples pour la caractérisation de tumeurs par IRM multiparamétrique. 47èmes Journées de Statistique de la SFdS, Jun 2015, Lille, France. ⟨hal-01253588⟩

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