Effective utilization of multi-domain data for brain disease identification has recently attracted increasing attention since a large number of subjects from multiple domains could be beneficial for investigating the pathological changes of disease-affected brains. Previous machine learning methods often suffer from inter-domain data heterogeneity caused by different scanning parameters. Although several deep learning methods have been developed, they usually assume that the source classifier can be directly transferred to the target (i.e., to-be-analyzed) domain upon the learned domain-invariant features, thus ignoring the shift in data distributions across different domains. Also, most of them rely on fully-labeled data in both target and source domains for model training, while labeled target data are generally unavailable. To this end, we present an Unsupervised Conditional consensus Adversarial Network (UCAN) for deep domain adaptation, which can learn the disease classifier from the labeled source domain and adapt to a different target domain (without any label information). The UCAN model contains three major components: (1) a feature extraction module for learning discriminate representations from the input MRI, (2) a cycle feature adaptation module to assist feature and classifier adaptation between the source and target domains, and (3) a classification module for disease identification. Experimental results on 1,506 subjects from ADNI1 (with 1.5 T structural MRI) and ADNI2 (with 3.0 T structural MRI) have demonstrated the effectiveness of the proposed UCAN method in brain disease identification, compared with state-of-the-art approaches.
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