[17] Sonali Dash and Manas Ranjan Senapati. Enhancing detection of retinal blood vessels by combined approach of dwt, tyler coye and gamma correction. Biomedical Signal Processing and Control, 57:101740, 2020.
[18] Behdad Dashtbozorg, Ana Maria Mendonça, and Aurélio Campilha. An automatic graph-based approach for artery/vein classification in retinal images. IEEE Transactions on Image Processing, 23(3):1073-1083, 2013.
[19] Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell. Long-term recurrent convolutional networks for visual recognition and description. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2625-2634, 2015.
[20] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
[21] Rolando Estrada, Michael J Allingham, Priyatham S Mettu, Scott W Cousins, Carlo Tomasi, and Sina Farsiu. Retinal artery-vein classification via topology estimation. IEEE transactions on medical imaging, 34(12):2518-2534, 2015.
[22] Zhun Fan and Jia-Jie Mo. Automated blood vessel segmentation based on de-noising auto-encoder and neural network. In 2016 International conference on machine learning and cybernetics (ICMLC), volume 2, pages 849–856. IEEE, 2016.
[23] Damian JJ Farnell, Fraser N Hatfield, Paul Knox, Michael Reakes, Stan Spencer, David Parry, and Simon P Harding. Enhancement of blood vessels in digital fundus photographs via the application of multiscale line operators. Journal of the Franklin institute, 345(7):748-765, 2008.
[24] Christoph Feichtenhofer. X3d: Expanding architectures for efficient video recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 203-213, 2020.
[25] Muhammad Moazam Fraz, Paolo Remagnino, Andreas Hoppe, Bunyarit Uyyanonvara, Alicja R Rudnicka, Christopher G Owen, and Sarah A Barman. An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Transactions on Biomedical Engineering, 59(9):2538-2548, 2012.
[26] Manuel E Gegundez-Arias, Diego Marin-Santos, Isaac Perez-Borrero, and Manuel J Vasallo-Vazquez. A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified u-net model. Computer Methods and Programs in Biomedicine, 205:106081, 2021.
[27] Georgia Gkioxari and Jitendra Malik. Finding action tubes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 759-768, 2015.
[28] Mahdi Hashemzadeh and Baharak Adlpour Azar. Retinal blood vessel extraction employing effective image features and combination of supervised and unsupervised machine learning methods. Artificial intelligence in medicine, 95:1-15, 2019.
[29] Ali Hatamizadeh, Hamid Hosseini, Niraj Patel, Jinseo Choi, Cameron C Pole, Cory M Hoeferlin, Steven D Schwartz, and Demetri Terzopoulos. Ravir: A dataset and methodology for the semantic segmentation and quantitative analysis of retinal arteries and veins in infrared reflectance imaging. IEEE Journal of Biomedical and Health Informatics, 26(7):3272-3283, 2022.
[30] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition, 2015.
[31] Qiao Hu, Michael D Abramoff, and Mona K Garvin. Automated separation of binary overlapping trees in low-contrast color retinal images. In Medical Image Computing and Computer-Assisted Intervention—MICCAI 2013: 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part II 16, pages 436-443. Springer, 2013.
[32] Usama Iqbal. Smartphone fundus photography: a narrative review. International Journal of Retina and Vitreous, 7(1):44, 2021.
[33] Zhexin Jiang, Hao Zhang, Yi Wang, and Seok-Bum Ko. Retinal blood vessel segmentation using fully convolutional network with transfer learning. Computerized Medical Imaging and Graphics, 68:1-15, 2018.