Automated detection of retinopathy of prematurity by deep attention network

Author:

Lei BaiyingORCID,Zeng Xianlu,Huang Shan,Zhang Rugang,Chen Guozhen,Zhao Jinfeng,Wang Tianfu,Wang Jiantao,Zhang Guoming

Funder

Major Research Plan

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Reference56 articles.

1. Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RP et al (2018) Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol 136(7):803–810

2. Burlina PM, Joshi N, Pekala M, Pacheco KD, Freund DE, Bressler NM (2017) Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol 135(11):1170–1176

3. Chen Y, Feng J, Gilbert C, Yin H, Liang J, Li X (2015) Time at treatment of severe retinopathy of prematurity in China: recommendations for guidelines in more mature infants. PLoS One 10(2):e0116669

4. Chen L-C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587

5. Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. Proc IEEE Europ Conf Comput Vis, pp 801–818

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