Domain-specific information preservation for Alzheimer's disease diagnosis with incomplete multi-modality neuroimages

Although multi-modality neuroimages have advanced the early diagnosis of Alzheimer's Disease (AD), missing modality issue still poses a unique challenge in the clinical practice. Recent studies have tried to impute the missing data so as to utilize all available subjects for training robust multi-modality models. However, these studies may overlook the modality-specific information inherent in multi-modality data, that is, different modalities possess distinct imaging characteristics and focus on different aspects of the disease. In this paper, we propose a domain-specific information preservation (DSIP) framework, consisting of modality imputation stage and status identification stage, for AD diagnosis with incomplete multi-modality neuroimages. In the first stage, a specificity-induced generative adversarial network (SIGAN) is developed to bridge the modality gap and capture modality-specific details for imputing high-quality neuroimages. In the second stage, a specificity-promoted diagnosis network (SPDN) is designed to promote the inter-modality feature interaction and the classifier robustness for identifying disease status accurately. Extensive experiments demonstrate the proposed method significantly outperforms state-of-the-art methods in both modality imputation and status identification tasks.