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Previous foundation models for fundus images were pre-trained with limited disease categories and knowledge base.
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2007
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Z. Zhang, F. S. Yin, J. Liu, W. K. Wong, N. M. Tan, B. H. Lee, J. Cheng, and T. Y. Wong, “Origa-light: An online retinal fundus image database for glaucoma analysis and research,” in 2010 Annual international conference of the IEEE engineering in medicine and biology . IEEE, 2010, pp. 3065–3068
2010
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G. Quellec, M. Lamard, G. Cazuguel, L. Bekri, W. Daccache, C. Roux, and B. Cochener, “Automated assessment of diabetic retinopathy severity using content-based image retrieval in multimodal fundus photographs,” Investigative ophthalmology & visual science , vol. 52, no. 11, pp. 8342–8348, 2011
2011
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K. W. Ng, G.-L. Tian, and M.-L. Tang, “Dirichlet and related distributions: Theory, methods and applications,” 2011
2011
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P. S. Chandakkar, R. Venkatesan, B. Li, and H. K. Li, “A machine-learning approach to retrieving diabetic retinopathy images,” in Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine , 2012, pp. 588–589
2012
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E. Decenciere, G. Cazuguel, X. Zhang, G. Thibault, J.-C. Klein, F. Meyer, B. Marcotegui, G. Quellec, M. Lamard, R. Danno et al. , “Teleophta: Machine learning and image processing methods for teleophthalmology,” Irbm , vol. 34, no. 2, pp. 196–203, 2013
2013
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2014
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2015
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A. Singh, M. K. Dutta, M. ParthaSarathi, V. Uher, and R. Burget, “Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image,” Computer methods and programs in biomedicine , vol. 124, pp. 108–120, 2016
2016
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R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 618–626
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2017
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2017
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J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. O’Donoghue, D. Visentin et al. , “Clinically applicable deep learning for diagnosis and referral in retinal disease,” Nature medicine , vol. 24, no. 9, pp. 1342–1350, 2018
2018
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V. Bellemo, Z. W. Lim, G. Lim, Q. D. Nguyen, Y. Xie, M. Y. Yip, H. Hamzah, J. Ho, X. Q. Lee, W. Hsu et al. , “Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in africa: a clinical validation study,” The Lancet Digital Health , vol. 1, no. 1, pp. e35–e44, 2019
2019
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H. Liu, L. Li, I. M. Wormstone, C. Qiao, C. Zhang, P. Liu, S. Li, H. Wang, D. Mou, R. Pang et al. , “Development and validation of a deep learning system to detect glaucomatous optic neuropathy using fundus photographs,” JAMA ophthalmology , vol. 137, no. 12, pp. 1353–1360, 2019
2019
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S. Taylor, J. M. Brown, K. Gupta, J. P. Campbell, S. Ostmo, R. P. Chan, J. Dy, D. Erdogmus, S. Ioannidis, S. J. Kim et al. , “Monitoring disease progression with a quantitative severity scale for retinopathy of prematurity using deep learning,” JAMA ophthalmology , vol. 137, no. 9, pp. 1022–1028, 2019
2019
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C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of big data , vol. 6, no. 1, pp. 1–48, 2019
2019
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2019
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A. Diaz-Pinto, S. Morales, V. Naranjo, T. Köhler, J. M. Mossi, and A. Navea, “Cnns for automatic glaucoma assessment using fundus images: an extensive validation,” Biomedical engineering online , vol. 18, pp. 1–19, 2019
2019
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Y. Xie, Q. D. Nguyen, H. Hamzah, G. Lim, V. Bellemo, D. V. Gunasekeran, M. Y. Yip, X. Q. Lee, W. Hsu, M. L. Lee et al. , “Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study,” The Lancet Digital Health , vol. 2, no. 5, pp. e240–e249, 2020
2020
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M. Wang, J. Tichelaar, L. R. Pasquale, L. Q. Shen, M. V. Boland, S. R. Wellik, C. G. De Moraes, J. S. Myers, P. Ramulu, M. Kwon et al. , “Characterization of central visual field loss in end-stage glaucoma by unsupervised artificial intelligence,” JAMA ophthalmology , vol. 138, no. 2, pp. 190–198, 2020
2020
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M. Zhang, S. X. Fei, J. Liu, S. Xu, Y. Piao, and H. Lu, “Asymmetric two-stream architecture for accurate rgb-d saliency detection,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVIII 16 . Springer, 2020, pp. 374–390
2020
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2020
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2020
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2020
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M. N. Bajwa, G. A. P. Singh, W. Neumeier, M. I. Malik, A. Dengel, and S. Ahmed, “G1020: A benchmark retinal fundus image dataset for computer-aided glaucoma detection,” in 2020 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2020, pp. 1–7
2020
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P. Porwal, S. Pachade, M. Kokare, G. Deshmukh, J. Son, W. Bae, L. Liu, J. Wang, X. Liu, L. Gao et al. , “Idrid: Diabetic retinopathy–segmentation and grading challenge,” Medical image analysis , vol. 59, p. 101561, 2020
2020
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J. I. Orlando, H. Fu, J. B. Breda, K. Van Keer, D. R. Bathula, A. Diaz-Pinto, R. Fang, P.-A. Heng, J. Kim, J. Lee et al. , “Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs,” Medical image analysis , vol. 59, p. 101570, 2020
2020
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Y. Peng, W. Zhu, Z. Chen, M. Wang, L. Geng, K. Yu, Y. Zhou, T. Wang, D. Xiang, F. Chen et al. , “Automatic staging for retinopathy of prematurity with deep feature fusion and ordinal classification strategy,” IEEE Transactions on Medical Imaging , vol. 40, no. 7, pp. 1750–1762, 2021
2021
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L.-P. Cen, J. Ji, J.-W. Lin, S.-T. Ju, H.-J. Lin, T.-P. Li, Y. Wang, J.-F. Yang, Y.-F. Liu, S. Tan et al. , “Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks,” Nature communications , vol. 12, no. 1, p. 4828, 2021
2021
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A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
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J. Fang, H. Fu, and J. Liu, “Deep triplet hashing network for case-based medical image retrieval,” Medical image analysis , vol. 69, p. 101981, 2021
2021
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D. Lin, J. Xiong, C. Liu, L. Zhao, Z. Li, S. Yu, X. Wu, Z. Ge, X. Hu, B. Wang et al. , “Application of comprehensive artificial intelligence retinal expert (care) system: a national real-world evidence study,” The Lancet Digital Health , vol. 3, no. 8, pp. e486–e495, 2021
2021
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B. Li, H. Chen, B. Zhang, M. Yuan, X. Jin, B. Lei, J. Xu, W. Gu, D. C. S. Wong, X. He et al. , “Development and evaluation of a deep learning model for the detection of multiple fundus diseases based on colour fundus photography,” British Journal of Ophthalmology , vol. 106, no. 8, pp. 1079–1086, 2022
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