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Retinal artery/vein (A/V) classification is a critical technique for diagnosing diabetes and cardiovascular diseases.
“Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs,”
Meindert Niemeijer, Xiayu Xu, Alina V Dumitrescu, Priya Gupta, Bram Van Ginneken, James C Folk, and Michael D Abramoff, · 2011
Earlier work this paper cites.
“Automated separation of binary overlapping trees in low-contrast color retinal images,”
Qiao Hu, Michael D Abràmoff, and Mona K Garvin, · 2013
Earlier work this paper cites.
“Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database,”
Jan Odstrcilik, Radim Kolar, Attila Budai, Joachim Hornegger, Jiri Jan, Jiri Gazarek, Tomas Kubena, Pavel Cernosek, Ondrej Svoboda, and Elli Angelopoulou, · 2013
Earlier work this paper cites.
“Temporal ensembling for semi-supervised learning,”
Samuli Laine and Timo Aila, · 2016
Earlier work this paper cites.
“An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image,”
Xiayu Xu, Wenxiang Ding, Michael D Abràmoff, and Ruofan Cao, · 2017
Earlier work this paper cites.
“Unsupervised domain adaptation in brain lesion segmentation with adversarial networks,”
Konstantinos Kamnitsas, Christian Baumgartner, Christian Ledig, Virginia Newcombe, Joanna Simpson, Andrew Kane, David Menon, Aditya Nori, Antonio Criminisi, Daniel Rueckert, et al., · 2017
Earlier work this paper cites.
“Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,”
Antti Tarvainen and Harri Valpola, · 2017
Cited alongside, same era.
“mixup: Beyond empirical risk minimization,”
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz, · 2017
Cited alongside, same era.
“Deep convolutional artery/vein classification of retinal vessels,”
Maria Ines Meyer, Adrian Galdran, Pedro Costa, Ana Maria Mendonça, and Aurélio Campilho, · 2018
Cited alongside, same era.
Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Ben Glocker, Xiahai Zhuang, and Pheng-Ann Heng, · 2018
Cited alongside, same era.
“Towards a glaucoma risk index based on simulated hemodynamics from fundus images,”
José Ignacio Orlando, Joao Barbosa Breda, Karel Van Keer, Matthew B Blaschko, Pablo J Blanco, and Carlos A Bulant, · 2018
“Multi-task neural networks with spatial activation for retinal vessel segmentation and artery/vein classification,”
Wenao Ma, Shuang Yu, Kai Ma, Jiexiang Wang, Xinghao Ding, and Yefeng Zheng, · 2019
Later among the works it cites.
“Uncertainty-aware artery/vein classification on retinal images,”
Adrian Galdran, M Meyer, P Costa, A Campilho, et al., · 2019
Later among the works it cites.
“Unsupervised domain adaptation for medical imaging segmentation with self-ensembling,”
Christian S Perone, Pedro Ballester, Rodrigo C Barros, and Julien Cohen-Adad, · 2019
Later among the works it cites.
“Cfea: Collaborative feature ensembling adaptation for domain adaptation in unsupervised optic disc and cup segmentation,”
Peng Liu, Bin Kong, Zhongyu Li, Shaoting Zhang, and Ruogu Fang, · 2019
Later among the works it cites.
“Semi-supervised semantic segmentation needs strong, high-dimensional perturbations,”
Geoff French, Timo Aila, Samuli Laine, Michal Mackiewicz, and Graham Finlayson, · 2019
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Cited alongside, same era.
“Retinal vascular network topology reconstruction and artery/vein classification via dominant set clustering,”
Yitian Zhao, Jianyang Xie, Huaizhong Zhang, Yalin Zheng, Yifan Zhao, Hong Qi, Yangchun Zhao, Pan Su, Jiang Liu, and Yonghuai Liu, · 2019
Cited alongside, same era.
Later among the works it cites.
“Curriculum domain adaptation for semantic segmentation of urban scenes,”
Yang Zhang, Philip David, and Boqing Gong, · 2030
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