Machine Learning
Joint estimation of multiple clinical variables of neurological diseases from imaging patterns
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Ordinal hyperplanes ranker with cost sensitivities for age estimation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Early diagnosis of Alzheimer's disease (AD) based on neuroimaging and fluid biomarker data has attracted a lot of interest in medical image analysis. Most existing studies have been focusing on two-class classification problems, e.g., distinguishing AD patients from cognitive normal (CN) elderly or distinguishing mild cognitive impairment (MCI) individuals from CN elderly. However, to achieve the goal of early diagnosis of AD, we need to identify individuals with AD and MCI, especially MCI individuals who will convert to AD, in a single setting, which essentially is a multi-class classification problem. In this paper, we propose an ordinal ranking based classification method for distinguishing CN, MCI non-converter (MCI-NC), MCI converter (MCI-C), and AD at an individual level, taking into account the inherent ordinal severity of brain damage caused by normal aging, MCI, and AD, rather than formulating the classification as a multi-class classification problem. Experiment results indicate that the proposed method can achieve a better performance than traditional multi-class classification techniques based on multimodal neuroimaging and CSF biomarker data of the ADNI.