Domain-specific information preservation for Alzheimer's disease diagnosis with incomplete multi-modality neuroimages.
Not reported in abstract.
Where it sits
this study against the rest of the dsip corpusSummary and findings
This study introduces a domain-specific information preservation (DSIP) framework for diagnosing Alzheimer's Disease (AD) using incomplete multi-modality neuroimages. The framework includes a modality imputation stage and a status identification stage, aiming to improve diagnostic accuracy. No specific numeric findings or outcomes are reported in the abstract.
Abstract
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.
Background
This paper addresses the challenge of diagnosing Alzheimer's disease using incomplete neuroimaging data. Previous research has indicated that multi-modality neuroimages can enhance diagnostic accuracy, but the effectiveness of these methods in the context of incomplete data remains underexplored. Understanding how to preserve domain-specific information could improve diagnostic capabilities in clinical settings.
Methods
Not reported in abstract.
Results
Not reported in abstract.
Interpretation
Not reported in abstract.
Key findings
- Not reported in abstract.
Limitations
- No specific findings reported in abstract.
- Lack of detail on study design and methodology.