Discriminant Convex Non-negative Matrix Factorization for the classification of human brain tumours |
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| Authors: | Albert Vilamala Paulo JG Lisboa Sandra Ortega-Martorell Alfredo Vellido |
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| Affiliation: | 1. Universitat Politècnica de Catalunya, Departament de Llenguatges i Sistemes Informàtics, Barcelona, Spain;2. Liverpool John Moores University, Department of Mathematics and Statistics, Liverpool, United Kingdom;3. Departament de Bioquímica i Biología Molecular, Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Barcelona, Spain;4. Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain;5. Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Barcelona, Spain |
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| Abstract: | 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. |
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| Keywords: | Discriminant Convex Non-negative Matrix Factorization Magnetic resonance spectroscopy Brain tumours Source separation |
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