Discriminant Convex Non-negative Matrix Factorization for the classification of human brain tumours
•Brain tumours can be diagnosed on the basis of magnetic resonance spectroscopy (MRS).•A new method to introduce class information into a convex variant of NMF is presented.•Novel techniques for diagnostic predictions of unseen MRS are described.•The new method and techniques are experimentally asse...
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| Published in: | Pattern recognition letters Vol. 34; no. 14; pp. 1734 - 1747 |
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| Main Authors: | , , , |
| Format: | Journal Article Publication |
| Language: | English |
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Elsevier B.V
15.10.2013
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| ISSN: | 0167-8655, 1872-7344 |
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| Abstract | •Brain tumours can be diagnosed on the basis of magnetic resonance spectroscopy (MRS).•A new method to introduce class information into a convex variant of NMF is presented.•Novel techniques for diagnostic predictions of unseen MRS are described.•The new method and techniques are experimentally assessed with real MRS data.•The new methods are predictive and generate very tumour type-specific MRS sources.
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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| AbstractList | •Brain tumours can be diagnosed on the basis of magnetic resonance spectroscopy (MRS).•A new method to introduce class information into a convex variant of NMF is presented.•Novel techniques for diagnostic predictions of unseen MRS are described.•The new method and techniques are experimentally assessed with real MRS data.•The new methods are predictive and generate very tumour type-specific MRS sources.
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. 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 |
| Author | Ortega-Martorell, Sandra Lisboa, Paulo J.G. Vilamala, Albert Vellido, Alfredo |
| Author_xml | – sequence: 1 givenname: Albert surname: Vilamala fullname: Vilamala, Albert email: avilamala@lsi.upc.edu organization: Universitat Politècnica de Catalunya, Departament de Llenguatges i Sistemes Informàtics, Barcelona, Spain – sequence: 2 givenname: Paulo J.G. surname: Lisboa fullname: Lisboa, Paulo J.G. organization: Liverpool John Moores University, Department of Mathematics and Statistics, Liverpool, United Kingdom – sequence: 3 givenname: Sandra surname: Ortega-Martorell fullname: Ortega-Martorell, Sandra organization: Departament de Bioquímica i Biología Molecular, Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Barcelona, Spain – sequence: 4 givenname: Alfredo surname: Vellido fullname: Vellido, Alfredo organization: Universitat Politècnica de Catalunya, Departament de Llenguatges i Sistemes Informàtics, Barcelona, Spain |
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| CitedBy_id | crossref_primary_10_1155_2014_608158 crossref_primary_10_1002_nbm_5020 crossref_primary_10_1002_nbm_3439 crossref_primary_10_1002_nbm_4193 crossref_primary_10_1038_s41598_020_76686_y crossref_primary_10_1109_TCSVT_2016_2539779 crossref_primary_10_3390_cancers15154002 crossref_primary_10_1016_j_eswa_2014_02_031 crossref_primary_10_1109_ACCESS_2018_2854232 crossref_primary_10_1371_journal_pone_0083773 |
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| Keywords | Magnetic resonance spectroscopy Discriminant Convex Non-negative Matrix Factorization Source separation Brain tumours |
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| Snippet | •Brain tumours can be diagnosed on the basis of magnetic resonance spectroscopy (MRS).•A new method to introduce class information into a convex variant of NMF... The medical analysis of human brain tumours commonly relies on indirect measurements. Among these, magnetic resonance imaging (MRI) and spectroscopy (MRS)... |
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| SubjectTerms | Aplicacions de la informàtica Bioinformàtica Brain tumours Diagnosis Discriminant Convex Non-negative Matrix Factorization Informàtica Magnetic resonance spectroscopy Models matemàtics Source separation Tumors Àrees temàtiques de la UPC |
| Title | Discriminant Convex Non-negative Matrix Factorization for the classification of human brain tumours |
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