TY - GEN
T1 - Regularized tensor factorization for multi-modality medical image classification
AU - Batmanghelich, Nematollah
AU - Dong, Aoyan
AU - Taskar, Ben
AU - Davatzikos, Christos
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2011
Y1 - 2011
N2 - This paper presents a general discriminative dimensionality reduction framework for multi-modal image-based classification in medical imaging datasets. The major goal is to use all modalities simultaneously to transform very high dimensional image to a lower dimensional representation in a discriminative way. In addition to being discriminative, the proposed approach has the advantage of being clinically interpretable. We propose a framework based on regularized tensor decomposition. We show that different variants of tensor factorization imply various hypothesis about data. Inspired by the idea of multi-view dimensionality reduction in machine learning community, two different kinds of tensor decomposition and their implications are presented. We have validated our method on a multi-modal longitudinal brain imaging study. We compared this method with a publically available classification software based on SVM that has shown state-of-the-art classification rate in number of publications.
AB - This paper presents a general discriminative dimensionality reduction framework for multi-modal image-based classification in medical imaging datasets. The major goal is to use all modalities simultaneously to transform very high dimensional image to a lower dimensional representation in a discriminative way. In addition to being discriminative, the proposed approach has the advantage of being clinically interpretable. We propose a framework based on regularized tensor decomposition. We show that different variants of tensor factorization imply various hypothesis about data. Inspired by the idea of multi-view dimensionality reduction in machine learning community, two different kinds of tensor decomposition and their implications are presented. We have validated our method on a multi-modal longitudinal brain imaging study. We compared this method with a publically available classification software based on SVM that has shown state-of-the-art classification rate in number of publications.
KW - Basis Learning
KW - Classification
KW - Multi-Modality
KW - Multi-view Learning
KW - Optimization
KW - Tensor factorization
UR - http://www.scopus.com/inward/record.url?scp=82255164591&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-23626-6_3
DO - 10.1007/978-3-642-23626-6_3
M3 - Conference contribution
C2 - 22003679
AN - SCOPUS:82255164591
SN - 9783642236259
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 17
EP - 24
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings
T2 - 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
Y2 - 18 September 2011 through 22 September 2011
ER -