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We present VisionFM, a foundation model pre-trained with 3.4 million ophthalmic images from 560,457 individuals, covering a broad range of ophthalmic diseases, modalities, imaging devices, and demography.
Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis
Tham, Y.-C. et al · 2014
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P. & Brox, T · 2015
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Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
Gulshan, V. et al · 2016
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Improved techniques for training gans
Salimans, T. et al · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S. & Sun, J · 2016
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Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes
Ting, D. S. W. et al · 2017
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Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks
Burlina, P. M. et al · 2017
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Deep learning is effective for classifying normal versus age-related macular degeneration oct images
Lee, C. S., Baughman, D. M. & Lee, A. Y · 2017
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Retinal lesion detection with deep learning using image patches
Lam, C., Yu, C., Huang, L. & Rubin, D · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Wu, Z., Xiong, Y., Yu, S. X. & Lin, D · 2018
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Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
Poplin, R. et al · 2018
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World report on vision
Organization, W. H. et al · 2019
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Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy
Sayres, R. et al · 2019
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From machine to machine: an oct-trained deep learning algorithm for objective quantification of glaucomatous damage in fundus photographs
Medeiros, F. A., Jammal, A. A. & Thompson, A. C · 2019
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Deep learning and glaucoma specialists: the relative importance of optic disc features to predict glaucoma referral in fundus photographs
Phene, S. et al · 2019
Cited alongside, same era.
Unsupervised embedding learning via invariant and spreading instance feature
Ye, M., Zhang, X., Yuen, P. C. & Chang, S.-F · 2019
Cited alongside, same era.
Estimated number of ophthalmologists worldwide (international council of ophthalmology update): will we meet the needs?
Resnikoff, S. et al · 2020
Cited alongside, same era.
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning
Varadarajan, A. V. et al · 2020
Cited alongside, same era.
Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection
Li, F. et al · 2020
Cited alongside, same era.
Rotation-oriented collaborative self-supervised learning for retinal disease diagnosis
Li, X. et al · 2021
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Screening and identifying hepatobiliary diseases through deep learning using ocular images: a prospective, multicentre study
Xiao, W. et al · 2021
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Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study
Ruamviboonsuk, P. et al · 2022
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Learning two-stream cnn for multi-modal age-related macular degeneration categorization
Wang, W. et al · 2022
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Deepdrid: Diabetic retinopathy—grading and image quality estimation challenge
Liu, R. et al · 2022
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Development and validation of deep learning models for screening multiple abnormal findings in retinal fundus images
Son, J. et al · 2020
Cited alongside, same era.
Adam: Automatic detection challenge on age-related macular degeneration, DOI: 10.21227/dt4f-rt59 (2020)
Fu, H. et al · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M. & Hinton, G · 2020
Cited alongside, same era.
Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms
Rim, T. H. et al · 2020
Cited alongside, same era.
A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations
Sabanayagam, C. et al · 2020
Cited alongside, same era.
A deep learning system for detecting diabetic retinopathy across the disease spectrum
Dai, L. et al · 2021
Cited alongside, same era.
Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study
Tan, T.-E. et al · 2021
Cited alongside, same era.
Evaluation of generative adversarial networks for high-resolution synthetic image generation of circumpapillary optical coherence tomography images for glaucoma
Kumar, A. J. S. et al · 2022
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(2023), O · 2023
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Kirillov, A. et al · 2023
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Large ai models in health informatics: Applications, challenges, and the future
Qiu, J. et al · 2023
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Foundation models for generalist medical artificial intelligence
Moor, M. et al · 2023
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A foundation model for generalizable disease detection from retinal images
Zhou, Y. et al · 2023
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A deep learning model for novel systemic biomarkers in photographs of the external eye: a retrospective study
Babenko, B. et al · 2023
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Qian, B. et al · 2023
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