Dual-Path and Multi-Scale Enhanced Attention Network for Retinal Diseases Classification Using Ultra-Wide-Field Images

doi:10.1109/ACCESS.2023.3273613

科研成果详情

题名	Dual-Path and Multi-Scale Enhanced Attention Network for Retinal Diseases Classification Using Ultra-Wide-Field Images
作者	Chen, Fangsheng 1,2; Ma, Shaodong 2; Hao, Jinkui 2; Liu, Mengting 3; Gu, Yuanyuan 2; Yi, Quanyong 4; Zhang, Jiong 2; Zhao, Yitian 2
发表日期	2023
发表期刊	IEEE ACCESS 影响因子和分区
语种	英语
原始文献类型	Article
关键词	Diseases Retina Lesions Feature extraction Deep learning Computer aided diagnosis Semantics Diseases classification UWF image multi-scale attention retina
其他关键词	DIABETIC-RETINOPATHY ; DEEP
摘要	Early computer-aided early diagnosis (CAD) based on retinal imaging is critical to the timely management and treatment planning of retina-related diseases. However, the inherent characteristics of retinal images and the complexity of their pathological patterns, such as low image contrast and different lesion sizes, restrict the performance of CAD systems. Recently, ultra-wide-field (UWF) retinal images have become a useful tool for disease detection due to the capability of capturing much broader view of retina (i.e., up to 200 degrees), in comparison with the most commonly used retinal fundus images (45 degrees). In this paper, we propose an attention-based multi-branch network for the diseases classification of four different subject groups. The proposed method consists of a multi-scale feature fusion module and a dual attention module. Specifically, small-scale lesions are identified using the features extracted from the multi-scale feature fusion module. To better explore the obtained features, the dual attention module with a global attention graph is incorporated to enable the network to recognize the salient objects of interest. Comprehensive validations on both private and public datasets were carried out to verify the effectiveness of the proposed model.
资助项目	Ningbo Natural Science Foundation[20221JCGY010608,2022J143];National Natural Science Foundation Program of China[61906181,62103398];Zhejiang Provincial Natural Science Foundation[LQ21F010007,LQ23F010002,LR22F020008,LZ19F010001,LZ23F010002];Youth Innovation Promotion Association CAS[2021298];Ningbo University of Technology[2019B10033,2019B10061,2021Z054,2022KQ29];
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
ISSN	2169-3536
EISSN	2169-3536
卷号	11 页码:45405-45415
DOI	10.1109/ACCESS.2023.3273613
页数	11
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS记录号	WOS:000988213400001
收录类别	SCIE ; EI ; SCOPUS
EI入藏号	20232114132801
EI主题词	Computer aided diagnosis
EI分类号	461.1 Biomedical Engineering ; 461.4 Ergonomics and Human Factors Engineering ; 461.6 Medicine and Pharmacology ; 716.1 Information Theory and Signal Processing ; 723.2 Data Processing and Image Processing ; 723.5 Computer Applications ; 901.2 Education ; 903.1 Information Sources and Analysis
URL	查看原文
SCOPUSEID	2-s2.0-85159752545
通讯作者地址	[Zhang, Jiong]Ningbo Institute of Materials Technology and Engineering,Chinese Academy of Sciences,Cixi Institute of Biomedical Engineering,Ningbo,315399,China ; [Zhao, Yitian]Ningbo Institute of Materials Technology and Engineering,Chinese Academy of Sciences,Cixi Institute of Biomedical Engineering,Ningbo,315399,China
Scopus学科分类	Computer Science (all);Materials Science (all);Engineering (all)
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/180038
专题	其他_附属宁波市眼科医院
通讯作者	Zhang, Jiong; Zhao, Yitian
作者单位	1.Zhejiang University of Technology,School of Mechanical and Engineering,Hangzhou,310014,China; 2.Ningbo Institute of Materials Technology and Engineering,Chinese Academy of Sciences,Cixi Institute of Biomedical Engineering,Ningbo,315399,China; 3.Sun Yat-sen University,School of Biomedical Engineering,Shenzhen,510275,China; 4.The Affiliated Ningbo Eye Hospital of Wenzhou Medical University,Ningbo,325035,China
推荐引用方式 GB/T 7714	Chen, Fangsheng,Ma, Shaodong,Hao, Jinkui,et al. Dual-Path and Multi-Scale Enhanced Attention Network for Retinal Diseases Classification Using Ultra-Wide-Field Images[J]. IEEE ACCESS,2023,11:45405-45415.
APA	Chen, Fangsheng., Ma, Shaodong., Hao, Jinkui., Liu, Mengting., Gu, Yuanyuan., ... & Zhao, Yitian. (2023). Dual-Path and Multi-Scale Enhanced Attention Network for Retinal Diseases Classification Using Ultra-Wide-Field Images. IEEE ACCESS, 11, 45405-45415.
MLA	Chen, Fangsheng,et al."Dual-Path and Multi-Scale Enhanced Attention Network for Retinal Diseases Classification Using Ultra-Wide-Field Images".IEEE ACCESS 11(2023):45405-45415.