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Título:
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A MAP approach for convex non-negative matrix factorization in the diagnosis of brain tumors
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Autor/a:
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Vilamala Muñoz, Albert; Belanche Muñoz, Luis Antonio; Vellido Alcacena, Alfredo
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. SOCO - Soft Computing |
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Abstract:
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Convex non-negative matrix factorization is a blind signal separation technique that has previously demonstrated to be well-suited for the task of human brain tumor diagnosis from magnetic resonance spectroscopy data. This is due to its ability to retrieve interpretable sources of mixed sign that highly correlate with tissue type prototypes. The current study provides a Bayesian formulation for such problem and derives a maximum a posteriori estimate based on a gradient descent algorithm specifically designed to deal with matrices with different sign restrictions. Its applicability to neuro-oncology diagnosis was experimentally assessed and the results were found to be comparable to those achieved by state of the art methods in tumor type discrimination and consistently better in source extraction. |
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Abstract:
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Peer Reviewed |
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Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Enginyeria del software
-Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
-Medicine--Data processing
-Engineering controlled terms: Bayesian networks
-Blind source separation
-Brain
-Magnetic resonance spectroscopy
-Neuroimaging
-Pattern recognition
-Praseodymium alloys
-Tumors
-Medicina--Informàtica |
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Derechos:
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Tipo de documento:
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Artículo - Versión publicada
Objeto de conferencia |
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