干旱区生态环境知识资源中心(Arid): RClaNet: An Explainable Alzheimer's Disease Diagnosis Framework by Joint Registration and Classification

Arid

DOI	10.1109/JBHI.2023.3337942
	RClaNet: An Explainable Alzheimer's Disease Diagnosis Framework by Joint Registration and Classification
	Wu, Liang; Hu, Shunbo; Wang, Duanwei; Liu, Changchun; Wang, Li
通讯作者	Liu, CC
来源期刊	IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
ISSN	2168-2194
EISSN	2168-2208
出版年	2024
卷号	28 期号:4 页码:2338-2349
英文摘要	Alzheimer's disease (AD) is an irreversible neurodegenerative disease that affects people's ability of daily life. Unfortunately, there is currently no known cure for AD. Thus, the early detection of AD plays a key role in preventing and controlling its progression. As one of representative methods for measuring brain atrophy, image registration technique has been widely adopted for AD diagnosis. In this study, an AD assistant diagnosis framework based on joint registration and classification is proposed. Specifically, to capture more local deformation information, a novel patch-based joint brain image registration and classification network (RClaNet) to estimate the local dense deformation fields (DDF) and disease risk probability maps (DRM) that explain high-risk areas for AD patients. RClaNet consists of a registration network and a classification network, in which the deformation field from registration network is fed into the classification network to enhance the prediction accuracy of the disease. Then, the exponential distance weighting method is used to obtain the global DDF and the global DRM without grid-like artifacts. Finally, the global classification network uses the global DRM for the early detection of AD. We evaluate the proposed method on the OASIS-3, AIBL, ADNI and COVID-19 datasets, and experimental results show that the proposed RClaNet achieves superior registration performances than several state-of-the-art methods. Early diagnosis of AD using the global DRM also yielded competitive results. These experiments prove that the deformation information in the registration process can be used to characterize subtle changes of degenerative diseases and further assist clinicians in diagnosis.
英文关键词	Diseases Deformation Image registration Medical diagnostic imaging Magnetic resonance imaging Atrophy Deep learning Alzheimer's disease registration classification atrophy deep learning
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001197865400050
WOS类目	Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics
WOS研究方向	Computer Science ; Mathematical & Computational Biology ; Medical Informatics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404137
推荐引用方式 GB/T 7714	Wu, Liang,Hu, Shunbo,Wang, Duanwei,et al. RClaNet: An Explainable Alzheimer's Disease Diagnosis Framework by Joint Registration and Classification[J],2024,28(4):2338-2349.
APA	Wu, Liang,Hu, Shunbo,Wang, Duanwei,Liu, Changchun,&Wang, Li.(2024).RClaNet: An Explainable Alzheimer's Disease Diagnosis Framework by Joint Registration and Classification.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,28(4),2338-2349.
MLA	Wu, Liang,et al."RClaNet: An Explainable Alzheimer's Disease Diagnosis Framework by Joint Registration and Classification".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 28.4(2024):2338-2349.