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  • Guorong Wu

Dynamic Hyper-Graph Inference Framework for Computer Assisted Diagnosis of Neurodegenerative Disease

Updated: Dec 2, 2018

IEEE Transaction on Medical Imaging, 2018

Illustration of conventional hyper-graph learning and our dynamic hyper-graph learning framework in computer-assisted diagnosis using multi-modal imaging data.

Abstract. Hyper-graph techniques have been widely investigated in computer vision and medical imaging applications, showing superior performance for modeling complex subject-wise relationships and sufficient flexibility to deal with missing data from multi-modal neuroimaging data. Existing hyper-graph methods, however, are inadequate for two reasons. One one hand, representations are generated only from the observed imaging data, a process that is completely independent of the subsequent data label inference/classification step. Thus hyper-graph results constructed this way may not be consistent with phenotype data such as clinical labels or scores and might generate sub-optimal predictions in relation to clinical labels/scores. On the other hand, current hyper-graph inference methods rely on two sequential steps: 1) building the hyper-graph for each individual modality and then predicted latent labels for new subjects upon each constructed hyper-graph, and 2) a voting procedure to incorporate inference results across different hyper-graphs. This approach, however, is limited by failing to consider the complex and complementary relationships of multi-modal imaging data with respect to hyper-graph inferential methods. To address these two issues, we propose a novel dynamic hyper-graph inference method supported by a semi-supervised framework. Our method iteratively estimates and adjusts the hyper-graph structures from multi-modal imaging data until consistency between the learned hyper-graph and the observed clinical labels and scores is achieved. This hyper-graph inference framework also eases the integration process of classification (identifying individuals having neurodegenerative disease) and regression (predicting the clinical scores) within the same framework. The experimental results on identifying MCI (Mild Cognition Impairment) subjects and the fine grained recognition of MCI progression stages show improved performance using our proposed hyper-graph inference method compared with conventional methods.

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University of North Carolina at Chapel Hill

Advanced Computational Medicine Laboratory

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