[1] [1] WU Y,YONG X,YANG S,et al.Multiscale network followed network model for retinal vessel segmentation［C］//LNCS 11071: Proceedings of the International Conference on Medical Image Computing and Computer-assisted Intervention,September 16-20,2018,Granada,Spain.Berlin,Heidelberg:Springer,2018:119-126.

[3] [3] LEE D H.Pseudo-label:the simple and efficient semi-supervised learning method for deep neural networks［C］//International Conference on Machine Learning,June 16-21,2013,Atlanta,USA.New York:ACM,2013,3(2):896-902.

[4] [4] XIE Q,DAI Z,HOVY E,et al.Unsupervised data augmentation for consistency training［EB/OL］.(2014-04-29)［2022-02-22］.http://arxiv.org/abs/1904.12848.

[5] [5] SOHN K,BERTHELOT D,LI C L,et al.Fixmatch:simplifying semi-supervised learning with consistency and confidence［EB/OL］.(2020-01-21)［2022-02-22］.http://arxiv.org/abs/2001.07685.

[6] [6] LI Y, PEI Z, LI J, CHEN D. Semi-supervised learning framework in segmentation of retinal blood vessel based on U-Net［C］//33rd Chinese Control and Decision Conference,May 22-24,2021,Kunming,China.New York:IEEE,2021:5972-5978.

[7] [7] LI C,MA W,SUN L,et al.Hierarchical deep network with uncertainty-aware semi-supervised learning for vessel segmentation［EB/OL］.(2021-05-31)［2022-02-22］.http://arxiv.org/abs/2105.14732.

[8] [8] TARVAINEN A,VALPOLA H.Mean teacher are better role models:weight-averaged consistency targets improve semi-supervised deep learning results［EB/OL］.(2021-01-21)［2022-02-22］.http://arxiv.org/abs/1703.01780,2017.

[9] [9] CUI W,LIU Y,LI Y,et al.Semi-supervised brain lesion segmentation with an adapted mean teacher model［C］//International Conference on Information Processing in Medical Imaging,July 2-7,2019,Hong Kong,China.Berlin,Heidelberg:Springer,2019:554-565.

[10] [10] ORLANDO J I,PROKOFYEVA E,BLASCHKO M B.A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images[J].IEEE Transactions on Biomedical Engineering,2016,64(1):16-27.

[11] [11] OKTAY O,SCHLEMPER J,FOLGOC L,et al.Attention u-net:learning where to look for the pancreas［EB/OL］.(2018-05-20)［2022-02-22］.http://arxiv.org/abs/1804.03999.

[12] [12] WANG B,QIU S,He H.Dual encoding u-net for retinal vessel segmentation［C］//LNCS 11764:International Conference on Medical Image Computing and Computer-Assisted Intervention,October 13-17,2019,Shenzhen,China.Berlin,Heidelberg:Springer,2019:84-92.

[13] [13] WANG D,HAYTHAM A,POTTENBURGH J,et al.Hard attention net for automatic retinal vessel segmentation[J].IEEE Journal of Biomedical and Health Informatics,2020,24(12):3384-3396.

[14] [14] NAVEED K,ABDULLAH F,MADNI H A,et al.Towards automated eye diagnosis:an improved retinal vessel segmentation framework using ensemble block matching 3D filter[J].Diagnostics,2021,11(1):114-141.

[15] [15] LIN Z,HUANG J,CHEN Y,et al.A high resolution representation network with multi-path scale for retinal vessel segmentation[J].Computer Methods and Programs in Biomedicine,2021,208(11):6206-6221.

[16] [16] ZHOU Y,CHEN Z,SHEN L,et al.A refined equilibrium generative adversarial network for retinal vessel segmentation[J].Neurocomputing,2021,437(19):118-130.

[17] [17] LI Y,YANG J,NI J,et al.TA-Net:triple attention network for medical image segmentation[J].Computers in Biology and Medicine,2021,137(10):4836-4848.

[18] [18] DU X,WANG J,SUN W.U-Net retinal blood vessel segmentation algorithm based on improved pyramid pooling method and attention mechanism[J].Physics in Medicine & Biology,2021,66(17):5013-5025.

[19] [19] HUANG G,LIU Z,LAURENS V D M,et al.Densely connected convolutional networks［C］//2017 IEEE Conference on Computer Vision and Pattern Recognition,July 21-26,2017,Honolulu,USA.Washington:Optical Society of America,2017:2261-2269.