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

Victor Mocioiu, Nuno M.Pedrosa De Barros, Sandra Ortega-Martorell, Johannes Slotboom, Urspeter Knecht, Carles Arús, Alfredo Vellido, Margarida Julià-Sapé

Research output: Other contribution

3 Citations (Scopus)

Abstract

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-Meansinitialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests.

Original languageAmerican English
Number of pages6
ISBN (Electronic)9782875870278
Publication statusPublished - 2016

Publication series

NameESANN 2016 - 24th European Symposium on Artificial Neural Networks

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