AVNet: A retinal artery/vein classification network with category-attention weighted fusion

doi:10.1016/j.cmpb.2020.105629

	AVNet: A retinal artery/vein classification network with category-attention weighted fusion
	Kang, Hong 1,2; Gao, Yingqi 1; Guo, Song 1; Xu, Xia 1; Li, Tao 1,3; Wang, Kai 1,4
	2020-10-01
发表期刊	COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
ISSN	0169-2607
卷号	195 页码:9
摘要	Background and Objective: Automatic artery/vein (A/V) classification in retinal images is of great importance in detecting vascular abnormalities, which may provide biomarkers for early diagnosis of many systemic diseases. It is intuitive to apply popular deep semantic segmentation network for A/V classification. However, the model is required to provide powerful representation ability since vessel is much more complex than general objects. Moreover, deep network may lead to inconsistent classification results for the same vessel due to the lack of structured optimization objective. Methods: In this paper, we propose a novel segmentation network named AVNet, which effectively enhances the classification ability of the model by integrating category-attention weighted fusion (CWF) module, significantly improving the pixel-level A/V classification results. Then, a graph based vascular structure reconstruction (VSR) algorithm is employed to reduce the segment-wise inconsistency, verifying the effect of the graph model on noisy vessel segmentation results. Results: The proposed method has been verified on three datasets, i.e. DRIVE, LES-AV and WIDE. AVNet achieves pixel-level accuracies of 90.62%, 90.34%, and 93.16%, respectively, and VSR further improves the performance by 0.19%, 1.85% and 0.64%, achieving the state-of-the-art results on these three datasets. Conclusion: The proposed method achieves competitive performance in A/V classification task. (C) 2020 Elsevier B.V. All rights reserved.
关键词	Retinal images Artery/vein classification Deep learning Graph model
DOI	10.1016/j.cmpb.2020.105629
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation[61872200] ; National Key Research and Development Program of China[2018YFB2100304] ; National Key Research and Development Program of China[2016YFC0400709] ; Open Project Fund of State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences[CARCH201905] ; Natural Science Foundation of Tianjin[19JCZDJC31600] ; Natural Science Foundation of Tianjin[18YFYZCG00060]
WOS研究方向	Computer Science ; Engineering ; Medical Informatics
WOS类目	Computer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods ; Engineering, Biomedical ; Medical Informatics
WOS记录号	WOS:000569804500011
出版者	ELSEVIER IRELAND LTD
引用统计	被引频次：17[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/15539
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Kai
作者单位	1.Nankai Univ, Coll Comp Sci, Tianjin, Peoples R China 2.Beijing Shanggong Med Technol Co Ltd, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 4.Key Lab Med Data Anal & Stat Res Tianjin, Tianjin, Peoples R China
推荐引用方式 GB/T 7714	Kang, Hong,Gao, Yingqi,Guo, Song,et al. AVNet: A retinal artery/vein classification network with category-attention weighted fusion[J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,2020,195:9.
APA	Kang, Hong,Gao, Yingqi,Guo, Song,Xu, Xia,Li, Tao,&Wang, Kai.(2020).AVNet: A retinal artery/vein classification network with category-attention weighted fusion.COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,195,9.
MLA	Kang, Hong,et al."AVNet: A retinal artery/vein classification network with category-attention weighted fusion".COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 195(2020):9.