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

Mocioiu, V and De Barros, NMP and Ortega-Martorell, S and Slotboom, J and Knecht, U and Arús, C and Vellido, A and Julià-Sapé, M (2016) A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases. In: ESANN 2016 - 24th European Symposium on Artificial Neural Networks , ES2016 (82). (ESANN 2016 - 24th European Symposium on Artificial Neural Networks, 27th - 29th April 2016, Bruges, Belgium).

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions: Applied Mathematics
Publisher: European Symposium on Artificial Neural Networks
Date Deposited: 07 Feb 2017 14:21
Last Modified: 07 Feb 2017 14:21
URI: http://researchonline.ljmu.ac.uk/id/eprint/5464

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