计算机科学
人工智能
域适应
领域(数学分析)
胶质瘤
磁共振成像
医学
数学
放射科
癌症研究
数学分析
分类器(UML)
作者
Luyue Yu,Ju Liu,Qiang Wu,Jing Wang,Aixi Qu
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:28 (1): 391-402
标识
DOI:10.1109/jbhi.2023.3332419
摘要
Accurate and fully automated brain structure examination and prediction from 3D volumetric magnetic resonance imaging (MRI) is a necessary step in medical imaging analysis, which can assist greatly in clinical diagnosis. Traditional deep learning models suffer from severe performance degradation when applied to clinically acquired unlabeled data. The performance degradation is mainly caused by domain discrepancy such as different device types and parameter settings for data acquisition. However, existing approaches focus on the reduction of domain discrepancies but ignore the entanglement of semantic features and domain information. In this article, we explore the feature invariance of categories and domains in different projection spaces and propose a Siamese-Transport Domain Adaptation (STDA) method using a joint optimal transport theory and contrastive learning for automatic 3D MRI classification and glioma multi-grade prediction. Specifically, the learning framework updates the distribution of features across domains and categories by Siamese transport network training with an Optimal Cost Transfer Strategy (OCTS) and a Mutual Invariant Constraint (MIC) in two projective spaces to find multiple invariants in potential heterogeneity. We design three sets of transfer task scenarios with different source and target domains, and demonstrate that STDA yields substantially higher generalization performance than other state-of-the-art unsupervised domain adaptation (UDA) methods. The method is applicable on 3D MRI data from glioma to Alzheimer's disease and has promising applications in the future clinical diagnosis and treatment of brain diseases.
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