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Automatic relevance source determination in human brain tumors using Bayesian NMF.

Ortega-Martorell, S and Olier, I and Julià-Sapé, M and Arús, C and Lisboa, P (2014) Automatic relevance source determination in human brain tumors using Bayesian NMF. In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) . pp. 99-104. (2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 9th-12th December 2014, Orlando, Florida).

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Abstract

The clinical management of brain tumors is very sensitive; thus, their non-invasive characterization is often preferred. Non-negative Matrix Factorization techniques have been successfully applied in the context of neuro-oncology to extract the underlying source signals that explain different tissue tumor types, for which knowing the number of sources to calculate was always required. In the current study we estimate the number of relevant sources for a set of discrimination problems involving brain tumors and normal brain. For this, we propose to start by calculating a high number of sources using Bayesian NMF and automatically discarding the irrelevant ones during the iterative process of matrices decomposition, hence obtaining a reduced range of interpretable solutions. The real data used in this study come from a widely tested human brain tumor database. Simulated data that resembled the real data was also generated to validate the hypothesis against ground truth. The results obtained suggest that the proposed approach is able to provide a small range of meaningful solutions to the problem of source extraction in human brain tumors.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Applied Mathematics
Publisher: IEEE
Date Deposited: 18 Mar 2016 11:13
Last Modified: 07 Oct 2016 11:01
DOI or Identification number: 10.1109/CIDM.2014.7008654
URI: http://researchonline.ljmu.ac.uk/id/eprint/3284

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