• A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases 

    Mocioiu, Victor; de Barros, Nuno M. Pedrosa; Ortega Martorell, Sandra; Slotboom, Johannes; Knecht, Urspeter; Arús, Carles; Vellido Alcacena, Alfredo; Julià Sapé, Margarida (I6doc.com, 2016)
    Text en actes de congrés
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    Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to ...
  • Classification, dimensionality reduction, and maximally discriminatory visualization of a multicentre 1H-MRS database of brain tumors 

    Lisboa, Paulo J.G.; Romero Merino, Enrique; Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Arús, Carles (IEEE, 2008)
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    The combination of an Artificial Neural Network classifier, a feature selection process, and a novel linear dimensionality reduction technique that provides a data projection for visualization and which preserves completely ...
  • Convex non-negative matrix factorization for brain tumor delimitation from MRSI data 

    Ortega Martorell, Sandra; Lisboa, Paulo J.G.; Vellido Alcacena, Alfredo; Simoes, Rui V.; Pumarola, Martí; Julià Sapé, Margarida; Arús, Carles (2012-10-23)
    Article
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  • Discriminating glioblastomas from metastases in a SV1H-MRS brain tumour database 

    Romero Merino, Enrique; Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Arús, Carles (2009)
    Text en actes de congrés
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    A Feature Selection (FS) process with a simple Machine Learning method, namely the Single-Layer Perceptron (SLP), is shown to discriminate metastases from glioblastomas with high accuracy using single voxel H-MRS from an ...
  • Exploratory characterization of a multi-centre 1H-MRS brain tumour database 

    Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Romero Merino, Enrique; Arús, Carles (Future Technology Press, 2009-01-31)
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    Non-invasive techniques such asMagnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) are often required for the diagnosis of tumours for which conclusive biopsies are not commonly available.While ...
  • Exploratory characterization of outliers in a multi-centre 1H-MRS brain tumour dataset 

    Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Romero Merino, Enrique; Arús, Carles (2008-09)
    Article
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    As part of the AIDTumour research project, the analysis of MRS data corresponding to various tumour pathologies is used to assist expert diagnosis. The high dimensionality of the MR spectra might obscure atypical aspects ...
  • Rule-based assistance to brain tumour diagnosis using LR-FIR 

    Nebot Castells, M. Àngela; Castro Espinoza, Félix Agustín; Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Arús, Carles (2008-09)
    Article
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    This paper describes a process of rule-extraction from a multi-centre brain tumour database consisting of nuclear magnetic res- onance spectroscopic signals. The expert diagnosis of human brain tumours can benefit from ...
  • Rule-based assistance to brain tumour diagnosis using LR-FIR 

    Nebot Castells, M. Àngela; Castro Espinoza, Félix Agustín; Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Arús, Carles (Future Technology Press, 2009-01-31)
    Capítol de llibre
    Accés restringit per política de l'editorial
    This paper describes a process of rule-extraction from a multi-centre brain tumour database consisting of nuclear magnetic res- onance spectroscopic signals. The expert diagnosis of human brain tumours can benefit from ...