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

Type of publication Peer-reviewed
Publikationsform Proceedings (peer-reviewed)
Publication date 2016
Author Mocioiu Victor, Pedrosa de Barros Nuno, Ortega-Martorell Sandra, Slotboom Johannes, Knecht Urspeter, ArúsCarles, Vellido Alfredo, Julià-Sapé Margarida ,
Project Establishing Novel MR Criteria for the Assessment of Malignant Glioma Progression
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Proceedings (peer-reviewed)

Title of proceedings European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Place Bruges, Belgium

Open Access


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 become mainstream in medical practice. In this brief paper, we define a machine learning-based analysis pipeline for helping in a difficult problem in the field of neuro-oncology, namely the discrimination of brain glioblastomas from single brain metastases. This pipeline involves source extraction using k-Means-initialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests.