– S. Takerkart (LIF) : Learning from structured fMRI patterns using graph kernels Carte non disponible Date/heure Date(s) - 27 septembre 2012 Catégories Pas de Catégories Learning from structured fMRI patterns using graph kernels. By Sylvain Takerkart, LIF. Classification of medical images in multi-subjects settings is a difficult challenge due to the variability that exists between individuals. Here we introduce a new graph-based framework specifically designed to deal with inter-subject functional variability present in functional MRI data. A graphical representation is built to encode the functional, geometric and structural properties of local activation patterns. The design of a specific graph kernel allows to conduct SVM classification directly in graph space. I will present results obtained on both simulated and real datasets, describe potential applications and discuss future directions for this work.
– S. Takerkart (LIF) : Learning from structured fMRI patterns using graph kernels Carte non disponible Date/heure Date(s) - 27 septembre 2012 Catégories Pas de Catégories Learning from structured fMRI patterns using graph kernels.\nBy Sylvain Takerkart, LIF.\n\nClassification of medical images in multi-subjects settings is a difficult challenge due to the variability that exists between individuals. Here we introduce a new graph-based framework specifically designed to deal with inter-subject functional variability present in functional MRI data. A graphical representation is built to encode the functional, geometric and structural properties of local activation patterns. The design of a specific graph kernel allows to conduct SVM classification directly in graph space. I will present results obtained on both simulated and real datasets, describe potential applications and discuss future directions for this work.[
