Paper Info
Title
Detection of corpus callosum malformations in pediatric population using the discriminative direction in multiple kernel learning.
Abstract
In this paper we propose a Multiple Kernel Learning (MKL) classifier to detect malformations of the Corpus Callosum (CC) and apply it in a pediatric population. Furthermore, we extend the concept of discriminative direction to the linear MKL methods, implementing it in a single subject analysis framework. The CC is characterized using different measures derived from Magnetic Resonance Imaging (MRI) data and the MKL approach is used to efficiently combine them. The discriminative direction analysis highlights those features that lead the classification for each given subject. In the case of a CC with malformation this means highlighting the abnormal characteristics of the CC that guide the diagnosis. Experiments show that the method correctly identifies the malformative aspects of the CC. Moreover, it is able to identify dishomogeneus, localized or widespread abnormalities among the different features. The proposed method is therefore suitable for supporting neuroradiologists in the decision-making process, providing them not only with a suggested diagnosis, but also with a description of the pathology.
Year
DOI
Venue
2014
10.1007/978-3-319-10470-6_38
Lecture Notes in Computer Science
Keywords
Field
DocType
magnetic resonance imaging,multiple kernel learning,brain imaging,computer-aided diagnosis
Population,Pattern recognition,Computer science,Multiple kernel learning,Computer-aided diagnosis,Speech recognition,Artificial intelligence,Neuroimaging,Corpus callosum,Classifier (linguistics),Discriminative model
Conference
Volume
Issue
ISSN
8674
Pt 2
0302-9743
Citations 
PageRank 
References 
1
0.37
8
Authors
5
Name
Order
Citations
PageRank
Denis Peruzzo1133.24
Filippo Arrigoni211.72
Fabio Triulzi310.70
Cecilia Parazzini410.70
Umberto Castellani568748.51