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Regularized tensor factorization for multi-modality medical image classification.

Author information

1
Section for Biomedical Image Analysis, Suite 380, 3600 Market St., 19104 Philadelphia, USA. batmangh@seas.upenn.edu

Abstract

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.

PMID:
22003679
[Indexed for MEDLINE]

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