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Discriminant convex non-negative matrix factorization for the classification of human brain tumours
Vilamala Muñoz, Albert; Lisboa, Paulo J G; Ortega Martorell, Sandra; Vellido Alcacena, Alfredo
Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. SOCO - Soft Computing
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 has been successfully accomplished using Non-negative Matrix Factorization (NMF) methods. In this paper, we present a method to introduce the available class information into the unsupervised source extraction process of a convex variant of NMF. Novel techniques to generate diagnostic predictions for new, unseen spectra using the proposed Discriminant Convex-NMF are also described and experimentally assessed.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
Tumors -- Diagnosis
Brain tumours
Discriminant Convex Non-negative Matrix Factorization
Magnetic resonance spectroscopy
Source separation
Tumors -- Models matemàtics
info:eu-repo/semantics/publishedVersion
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