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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
URL
https://upcommons.upc.edu/bitstream/handle/2117/97584/es2016-82.pdf
Type of Open Access
Website
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-Means-initialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests.
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