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 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.
Hinrichs
Chris
C
Dept. of Computer Sciences, University of Wisconsin-Madison, USA. hinrichs@cs.wisc.edu
Singh
Vikas
V
Xu
Guofan
G
Johnson
Sterling
S
D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis*; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique