MKL for Robust Multi-modality AD Classification

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 m...

• Type: Conference
• Year: 2009
• Where: MICCAI
• Authors: Chris Hinrichs, Vikas Singh, Guofan Xu, Sterling Johnson
• Keywords: AD Classification Problem, Maximum Margin Classifier, Medical Imaging, MICCAI 2009, Promising Multi-modal Classification