MKL for robust multi-modality AD classification.
MKL for robust multi-modality AD classification.
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DOI：
10.1007/978-3-642-04271-3_95
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Johnson, Sterling
中科院分区：
文献类型：
--
作者：
Hinrichs, Chris;Singh, Vikas;Xu, Guofan;Johnson, Sterling
我们研究的问题分类轻度阿尔茨海默氏病（AD）的健康个体（对照）使用多模态图像数据，以促进早期识别AD相关的病理。最近的几篇论文已经证明，这种分类是可能的MR或PET图像，使用机器学习方法，如SVM和提升。这些算法使用一种类型的图像数据来学习分类器。然而，AD不能单独通过一种成像方式很好地表征，并且通常使用几种图像类型进行分析-每种图像类型测量不同类型的结构/功能特征。本文探讨了同时使用多种模态的AD分类问题。这里的困难是评估每个模态的相关性（不能先验地假设），以及优化分类器。为了解决这个问题，我们利用和适应最近开发的称为多核学习（MKL）的想法。简而言之，每个成像模态产生一个（或多个内核），并且我们同时求解内核权重和最大边缘分类器。为了使模型的鲁棒性，我们提出了策略，以抑制一个小子集的离群值的分类器的影响-这产生了一个替代的最小化为基础的算法，强大的MKL。我们提出了有前途的多模态分类实验的大型数据集的图像从ADNI项目。
We study the problem of classifying mild Alzheimer’s disease (AD) subjects from healthy individuals (controls) using multi-modal image data, to facilitate early identification of AD related pathologies. Several recent papers have demonstrated that such classification is possible with MR or PET images, using machine learning methods such as SVM and boosting. These algorithms learn the classifier using one type of image data. However, AD is not well characterized by one imaging modality alone, and analysis is typically performed using several image types – each measuring a different type of structural/functional characteristic. This paper explores the AD classification problem using multiple modalities simultaneously. The difficulty here is to assess the relevance of each modality (which cannot be assumed a priori), as well as to optimize the classifier. To tackle this problem, we utilize and adapt a recently developed idea called Multi-Kernel learning (MKL). Briefly, each imaging modality spawns one (or more kernels) and we simultaneously solve for the kernel weights and a maximum margin classifier. To make the model robust, we propose strategies to suppress the influence of a small subset of outliers on the classifier – this yields an alternative minimization based algorithm for robust MKL. We present promising multi-modal classification experiments on a large dataset of images from the ADNI project.
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