Automated quality control for proton magnetic resonance spectroscopy data using convex non-negative matrix factorization

Victor Mocioiu, Sreenath P. Kyathanahally, Carles Arús, Alfredo Vellido, Margarida Julià-Sapé*

*Corresponding author for this work

Research output: Other contribution

5 Citations (Scopus)

Abstract

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.

Original languageAmerican English
Number of pages9
DOIs
Publication statusPublished - 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9656
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Brain tumors
  • Convex non-negative matrix factorization
  • Machine learning
  • Magnetic resonance spectroscopy
  • Pattern recognition
  • Quality control

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