Difference between revisions of "TIBM: Group comparison"
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* An approach combining dimension reduction and statistical modeling in the reduced space was proposed for the analysis of 4th order tensor, with application to the comparison of an individual with a population and the comparison of two populations [ https://icube-publis.unistra.fr/4-GRHK15 4-GRHK15]. | * An approach combining dimension reduction and statistical modeling in the reduced space was proposed for the analysis of 4th order tensor, with application to the comparison of an individual with a population and the comparison of two populations [ https://icube-publis.unistra.fr/4-GRHK15 4-GRHK15]. | ||
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* Finally, an original approach based on Gaussian Markov Random Field modeling has been proposed for comparing an individual to a normal statistical model with application to the detection of maxillofacial abnormalities [https://icube-publis.unistra.fr/2-Fais12 2-Fais12]. | * Finally, an original approach based on Gaussian Markov Random Field modeling has been proposed for comparing an individual to a normal statistical model with application to the detection of maxillofacial abnormalities [https://icube-publis.unistra.fr/2-Fais12 2-Fais12]. |
Revision as of 17:50, 7 October 2016
Group comparison consists in highlighting the differences between two populations of individuals, with the objective to develop new knowledge in neuroscience. The problem of comparing an individual with a normal model (atlas) is also addressed with the aim to identify pathological areas in a given subject.
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PhD thesis
- A. Bouchon, Modèles de régression multivariés pour la comparaison de populations en IRM de diffusion, septembre 2016
- T. Gkamas, Statistical modelling of high order tensors in diffusion weighted magnetic resonance imaging, septembre 2015
- G. Sfikas, Modèles statistiques non linéaires pour l'analyse de formes. Application à l'imagerie cérébrale, septembre 2012
- A. Belghith, Indexation de spectres HSQC et d’images IRMf appliquée à la détection de bio-marqueurs, mars 2012
- F. Renard, Création et utilisation d'atlas en IRM de diffusion. Application à l'étude des troubles de la conscience, septembre 2011
- M. Brucher, Représentations compactes et apprentissage non supervisé de variétés non linéaires : application au traitement d’images, octobre 2008
- T. Vik, Modèles statistiques d'apparence non gaussiens. Application à la création d'un atlas probabiliste de perfusion cérébrale en imagerie médicale., septembre 2004