TY - CHAP
T1 - Automated Quality Control for Proton Magnetic Resonance Spectroscopy Data Using Convex Non-negative Matrix Factorization.
AU - Mocioiu, Victor
AU - Kyathanahally, Sreenath P.
AU - Arús, Carles
AU - Vellido, Alfredo
AU - Julià-Sapé, Margarida
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2016/3
Y1 - 2016/3
N2 - 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.
AB - 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.
KW - Brain tumors
KW - Convex non-negative matrix factorization
KW - Machine learning
KW - Magnetic resonance spectroscopy
KW - Pattern recognition
KW - Quality control
UR - http://www.scopus.com/inward/record.url?scp=84973880400&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-31744-1_62
DO - 10.1007/978-3-319-31744-1_62
M3 - Chapter
AN - SCOPUS:84973880400
SN - 9783319317434
VL - 9656
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 719
EP - 727
BT - Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science()
ER -