2009 | OriginalPaper | Buchkapitel
MKL for Robust Multi-modality AD Classification
verfasst von : Chris Hinrichs, Vikas Singh, Guofan Xu, Sterling Johnson
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009
Verlag: Springer Berlin Heidelberg
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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.