Automated quality control for proton magnetic resonance spectroscopy data using convex non-negative matrix factorization
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Proton Magnetic Resonance Spectroscopy (1H MRS) has proven its diagnostic potential in a variety of conditions. However, MRS is not yet widely used in clinical routine because of the lack of experts on its diagnostic interpretation. Although data-based decision support systems exist to aid diagnosis, they often take for granted that the data is of good quality, which is not always the case in a real application context. Systems based on models built with bad quality data are likely to underperform in their decision support tasks. In this study, we propose a system to filter out such bad quality data. It is based on convex Non-Negative Matrix Factorization models, used as a dimensionality reduction procedure, and on the use of several classifiers to discriminate between good and bad quality data.
CitationMocioiu, V., Kyathanahally, S., Arús, C., Vellido, A., Julià, M. Automated quality control for proton magnetic resonance spectroscopy data using convex non-negative matrix factorization. A: International Work-Conference on Bioinformatics and Biomedical Engineering. "Bioinformatics and Biomedical Engineering: 4th International Conference, IWBBIO 2016, Granada, Spain, April 20-22, 2016: proceedings". Granada: Springer, 2016, p. 719-727.