[5] Wang D Y, Haytham A, Pottenburgh J, et al. Hard attention net for automatic retinal vessel segmentation[J]. IEEE J Biomed Health Inform, 2020, 24(12): 3384-3396.
[6] Han K, Wang Y H, Tian Q, et al. GhostNet: more features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 1577-1586.
[7] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018: 7132-7141.
[8] Zheng X W, Huan L X, Xia G S, et al. Parsing very high resolution urban scene images by learning deep ConvNets with edge-aware loss[J]. ISPRS J Photogrammetry Remote Sens, 2020, 170: 15-28.
[9] Zhang S D, He F Z. DRCDN: learning deep residual convolutional dehazing networks[J]. Vis Comput, 2020, 36(9): 1797-1808.
[10] Wang Y J, Hu S Y, Wang G D, et al. Multi-scale dilated convolution of convolutional neural network for crowd counting[J]. Multimed Tools Appl, 2020, 79(1-2): 1057-1073.
[11] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[C]//18th International Conference on Medical Image Computing and Computer Assisted Intervention. 2015: 234-241.
[12] Azad R, Asadi-Aghbolaghi M, Fathy M, et al. Bi-directional ConvLSTM U-Net with densley connected convolutions[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops. 2019: 406-415.
[13] Jha D, Riegler M A, Johansen D, et al. DoubleU-Net: a deep convolutional neural network for medical image segmentation[C]//2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). 2020: 558-564.
[14] Jiang Y, Wang F L, Gao J, et al. Efficient BFCN for automatic retinal vessel segmentation[J]. J Ophthalmol, 2020, 2020: 6439407.
[15] Yan Z Q, Yang X, Cheng K T. Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation[J]. IEEE Trans Biomed Eng, 2018, 65(9): 1912-1923.
[16] Li H, Wang Y K, Wan C, et al. MAU-Net: a retinal vessels segmentation method[C]//2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2020: 1958-1961.
[17] Jin Q G, Meng Z P, Pham T D, et al. DUNet: a deformable network for retinal vessel segmentation[J]. Knowl Based Syst, 2019, 178: 149-162.
[18] Li X, Jiang Y C, Li M L, et al. Lightweight attention convolutional neural network for retinal vessel image segmentation[J]. IEEE Trans Industr Inform, 2021, 17(3): 1958-1967.
[19] Tang P, Liang Q K, Yan X T, et al. Multi-proportion channel ensemble model for retinal vessel segmentation[J]. Comput Biol Med, 2019, 111: 103352.
[20] Oliveira A, Pereira S, Silva C A. Retinal vessel segmentation based on fully convolutional neural networks[J]. Expert Syst Appl, 2018, 112: 229-242.
[21] Wu Y C, Xia Y, Song Y, et al. NFN+: a novel network followed network for retinal vessel segmentation[J]. Neural Netw, 2020, 126: 153-162.
[22] Guo F, Li W Q, Kuang Z H, et al. MES-Net: a new network for retinal image segmentation[J]. Multimed Tools Appl, 2021, 80(10): 14767-14788.
[23] Tang Y, Rui Z Y, Yan C F, et al. ResWnet for retinal small vessel segmentation[J]. IEEE Access, 2020, 8: 198265-198274.
[24] Samuel P M, Veeramalai T. Multilevel and multiscale deep neural network for retinal blood vessel segmentation[J]. Symmetry, 2019, 11(7): 946.
