Learning Latent Structure Over Deep Fusion Model Of Mild Cognitive Impairment
2020 IEEE 17TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2020)(2020)
摘要
Many computational models have been developed to understand Alzheimer's disease ( AD) and its precursor mild cognitive impairment (MCI) using non-invasive neural imaging techniques, i.e. magnetic resonance imaging (MRI) based imaging modalities. Most existing methods focused on identification of imaging biomarkers, classification/ prediction of different clinical stages, regression of cognitive scores, or their combination as multi-task learning. Given the widely existed individual variability, however, it is still challenging to consider different learning tasks simultaneously even they share a similar goal: exploring the intrinsic alteration patterns in AD/MCI patients. Moreover, AD is a progressive neurodegenerative disorder with a long preclinical period. Besides conducting simple classification, brain changes should be considered within the entire AD/MCI progression process. Here, we introduced a novel deep fusion model for MCI using functional MRI data. We integrated autoencoder, multi-class classification and structure learning into a single deep model. During the modeling, different clinical groups including normal controls, early MCI and late MCI are considered simultaneously. With the learned discriminative representations, we not only can achieve a satisfied classification performance, but also construct a tree structure of MCI progressions.
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关键词
autoencoder, multi-class classification, deep fusion, fMRI
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