A Novel Dynamic Hyper-graph Inference Framework for Computer Assisted Diagnosis of Neuro-Diseases

Recently hyper-graph learning gains increasing attention in medical imaging area since the hyper-graph, a generalization of a graph, opts to characterize the complex subject-wise relationship behind multi-modal neuroimaging data. However, current hyper-graph methods mainly have two limitations: (1) The data representation encoded in the hyper-graph is learned only from the observed imaging features for each modality separately. Therefore, the learned subject-wise relationships are neither consistent across modalities nor fully consensus with the clinical labels or clinical scores. (2) The learning procedure of data representation is completely independent to the subsequent classification step. Since the data representation optimized in the feature domain is not exactly aligned with the clinical labels, such independent step-by-step workflow might result in sub-optimal classification. To address these limitations, we propose a novel dynamic hyper-graph inference framework, working in a semi-supervised manner, which iteratively estimates and adjusts the subject-wise relationship from multi-modal neuroimaging data until the learned data representation (encoded in the hyper-graph) achieves largest consensus with the observed clinical labels and scores. It is worth noting our inference framework is also flexible to integrate classification (identifying individuals with neuro-disease) and regression (predicting the clinical scores). We have demonstrated the performance of our proposed dynamic hyper-graph inference framework in identifying MCI (Mild Cognition Impairment) subjects and the fine-grained recognition of different progression stage of MCI, where we achieve more accurate diagnosis result than conventional counterpart methods.