TY - GEN
T1 - A localized MKL method for brain classification with known intra-class variability
AU - Ulaş, Aydin
AU - Gönen, Mehmet
AU - Castellani, Umberto
AU - Murino, Vittorio
AU - Bellani, Marcella
AU - Tansella, Michele
AU - Brambilla, Paolo
N1 - Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - Automatic decisional systems based on pattern classification methods are becoming very important to support medical diagnosis. In general, the overall objective is to classify between healthy subjects and patients affected by a certain disease. To reach this aim, significant efforts have been spent in finding reliable biomarkers which are able to robustly discriminate between the two populations (i.e., patients and controls). However, in real medical scenarios there are many factors, like the gender or the age, which make the source data very heterogeneous. This introduces a large intra-class variation by affecting the performance of the classification procedure. In this paper we exploit how to use the knowledge on heterogeneity factors to improve the classification accuracy. We propose a Clustered Localized Multiple Kernel Learning (CLMKL) algorithm by encoding in the classication model the information on the clusters of apriory known stratifications. Experiments are carried out for brain classification in Schizophrenia. We show that our algorithm performs clearly better than single kernel Support Vector Machines (SVMs), linear MKL algorithms and canonical Localized MKL algorithms when the gender information is considered as apriori knowledge.
AB - Automatic decisional systems based on pattern classification methods are becoming very important to support medical diagnosis. In general, the overall objective is to classify between healthy subjects and patients affected by a certain disease. To reach this aim, significant efforts have been spent in finding reliable biomarkers which are able to robustly discriminate between the two populations (i.e., patients and controls). However, in real medical scenarios there are many factors, like the gender or the age, which make the source data very heterogeneous. This introduces a large intra-class variation by affecting the performance of the classification procedure. In this paper we exploit how to use the knowledge on heterogeneity factors to improve the classification accuracy. We propose a Clustered Localized Multiple Kernel Learning (CLMKL) algorithm by encoding in the classication model the information on the clusters of apriory known stratifications. Experiments are carried out for brain classification in Schizophrenia. We show that our algorithm performs clearly better than single kernel Support Vector Machines (SVMs), linear MKL algorithms and canonical Localized MKL algorithms when the gender information is considered as apriori knowledge.
KW - brain imaging
KW - computer-aided diagnosis
KW - localized multiple kernel learning
KW - magnetic resonance imaging
KW - schizophrenia
UR - http://www.scopus.com/inward/record.url?scp=84870039340&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84870039340&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-35428-1_19
DO - 10.1007/978-3-642-35428-1_19
M3 - Conference contribution
AN - SCOPUS:84870039340
SN - 9783642354274
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 152
EP - 159
BT - Machine Learning in Medical Imaging - Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Revised Selected Papers
PB - Springer-Verlag
T2 - 3rd International Workshop on Machine Learning in Medical Imaging, MLMI 2012, Held in conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
Y2 - 1 October 2012 through 1 October 2012
ER -