Date(s) : 27/09/2012 iCal
14 h 00 min - 15 h 00 min
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.
Site Nord, CMI