title: Regularized tensor factorization for multi-modality medical image classification.
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.
authors: Section for Biomedical Image Analysis, Suite 380, 3600 Market St., 19104 Philadelphia, USA. batmangh@seas.upenn.edu
authors: Nematollah
authors: N
authors: Batmanghelich
authors: Aoyan
authors: A
authors: Dong
authors: Ben
authors: B
authors: Taskar
authors: Christos
authors: C
authors: Davatzikos
mesh_terms: D000368:Aged; D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D003072:Cognition Disorders / Q000473:pathology; D016208:Databases, Factual; D003952:Diagnostic Imaging / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D012984:Software