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Discriminant convex non-negative matrix factorization for the classification of human brain tumours

Autor
Vilamala, A.; Lisboa, P.; Ortega, S.; Vellido, A.
Tipus d'activitat
Article en revista
Revista
Pattern recognition letters
Data de publicació
2013-10-15
Volum
34
Número
14
Pàgina inicial
1734
Pàgina final
1747
DOI
https://doi.org/10.1016/j.patrec.2013.05.023 Obrir en finestra nova
Projecte finançador
AIDTUMOUR: HERRAMIENTAS BASADAS EN METODOS DE INTELIGENCIA ARTIFICIAL PARA EL APOYO A LA DECISION EN
Mejora de los protocolos para diagnóstico y seguimiento de respuesta a terapia en tumores cerebrales mediante estrategias de imagen molecular basadas en resonancia magnética (SAF2011-23870)
Repositori
http://hdl.handle.net/2117/20446 Obrir en finestra nova
URL
http://www.sciencedirect.com/science/article/pii/S0167865513002213 Obrir en finestra nova
Resum
The medical analysis of human brain tumours commonly relies on indirect measurements. Among these, magnetic resonance imaging (MRI) and spectroscopy (MRS) predominate in clinical settings as tools for diagnostic assistance. Pattern recognition (PR) methods have successfully been used in this task, usually interpreting diagnosis as a supervised classification problem. In MRS, the acquired spectral signal can be analyzed in an unsupervised manner to extract its constituent sources. Recently, this ...
Citació
Vilamala, A. [et al.]. Discriminant convex non-negative matrix factorization for the classification of human brain tumours. "Pattern recognition letters", 15 Octubre 2013, vol. 34, núm. 14, p. 1734-1747.
Paraules clau
Brain Tumours, Discriminant Convex Non-negative Matrix Factorization, Magnetic Resonance Spectroscopy, Source Separation
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
SOCO - Soft Computing

Participants

  • Vilamala Muñoz, Albert  (autor)
  • Lisboa, Paulo J G  (autor)
  • Ortega Martorell, Sandra  (autor)
  • Vellido Alcacena, Alfredo  (autor)