Nonlinear Metric Learning for Alzheimer's Disease Diagnosis with Integration of Longitudinal Neuroimaging Features

Identifying neuroimaging biomarkers of Alzheimer’s disease (AD) is of great importance for diagnosis and prognosis of the disease. In this study, we develop a novel nonlinear metric learning method to improve biomarker identification for Alzheimer’s disease and its early stage Mild Cognitive Impairment (MCI). Formulated under a constrained optimization framework, the proposed method learns a smooth nonlinear feature space transformation that pulls the samples of the same class closer to each other while pushing different classes further away. The thin-plate spline (TPS) is chosen as the geometric model due to its remarkable versatility and representation power in accounting for sophisticated deformations. In addition, a multi-resolution patch-based feature selection strategy is proposed to extract both cross-sectional and longitudinal features from MR brain images. Using the ADNI dataset, we evaluate the effectiveness of the proposed metric learning and feature extraction strategies and demonstrate the improvements over the state-of-the-art solutions within the same category.