In this paper, we use the promising paradigm of Multiple Kernel Learning (MKL) to challenge the problem of biomarker evaluation for schizophrenia detection. We use eight different Regions of Interest (ROIs) extracted from Magnetic Resonance Images (MRIs). For each region we evaluate both tissue and geometric properties. We show that with MKL we not only obtain more accurate classifiers than using single source support vector machines (SVMs), feature concatenation and kernel averaging but also we evaluate the relevance of the brain biomarkers in predicting this disease. On a data set of 50 patients and 50 healthy controls we can achieve an increase of 7% accuracy compared to standard methods. Moreover, we are able to quantify the importance of each source of information by highlighting the synergies between the involved brain characteristics.

Biomarker Evaluation by Multiple Kernel Learning for Schizophrenia detection

CASTELLANI, Umberto;MURINO, Vittorio;Bellani Marcella;TANSELLA, Michele;
2012

Abstract

In this paper, we use the promising paradigm of Multiple Kernel Learning (MKL) to challenge the problem of biomarker evaluation for schizophrenia detection. We use eight different Regions of Interest (ROIs) extracted from Magnetic Resonance Images (MRIs). For each region we evaluate both tissue and geometric properties. We show that with MKL we not only obtain more accurate classifiers than using single source support vector machines (SVMs), feature concatenation and kernel averaging but also we evaluate the relevance of the brain biomarkers in predicting this disease. On a data set of 50 patients and 50 healthy controls we can achieve an increase of 7% accuracy compared to standard methods. Moreover, we are able to quantify the importance of each source of information by highlighting the synergies between the involved brain characteristics.
Pattern Recognition; Multiple Kernel Learning; Neuroimaging
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11562/476606
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