Batmanghelich Nematollah, Dong Aoyan, Taskar Ben, Davatzikos Christos
Section for Biomedical Image Analysis, Suite 380, 3600 Market St., 19104 Philadelphia, USA.
Med Image Comput Comput Assist Interv. 2011;14(Pt 3):17-24. doi: 10.1007/978-3-642-23626-6_3.
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.
本文提出了一种用于医学成像数据集中基于多模态图像分类的通用判别式降维框架。主要目标是同时使用所有模态，以判别方式将非常高维的图像转换为低维表示。除了具有判别性之外，所提出的方法还具有临床可解释的优点。我们提出了一种基于正则化张量分解的框架。我们表明，张量分解的不同变体暗示了关于数据的各种假设。受机器学习社区中多视图降维思想的启发，提出了两种不同类型的张量分解及其含义。我们在一项多模态纵向脑成像研究中验证了我们的方法。我们将该方法与基于支持向量机（SVM）的公开可用分类软件进行了比较，该软件在多篇出版物中显示出了最先进的分类率。
