IFLP   13074
INSTITUTO DE FISICA LA PLATA
Unidad Ejecutora - UE
congresos y reuniones científicas
Título:
Diffuse Outlier Detection Technique for Functional Magnetic Resonance Imaging
Autor/es:
JAVIER GIACOMANTONE; T. TARUTINA; ARMANDO DE GIUSTI
Lugar:
Buenos Aires
Reunión:
Congreso; XVI Congreso Argentino de Ciencias de la ComputaciĆ³n. I Workshop: Procesamiento de SeƱales y Sistemas de Tiempo Real; 2010
Resumen:
We propose a new support vector machine (SVM) based method that improves the time series classi cation in magnetic resonance imaging (fMRI). We exploit the robust anisotropic di usion (RAD) technique to increase the classi cation performance of the one class support vector machine by taking into account the hypothesis of spatial relationshipbetween active voxels. The proposed method was called Diff use OneClass Support Vector Machine (DOCSVM). DOCSVM method treats activated voxels as outliers and applies one class support vector machineto generate an activation map and RAD to include the neighborhoodhypothesis, improving the classi cation and reducing the iteration stepswith respect to RADSPM. We give a brief review of the main methods,present receiver operating characteristic (ROC) results and conclude suggesting further research alternatives.
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