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The prevalence of vision-threatening eye diseases is a significant global burden, with many cases remaining undiagnosed or diagnosed too late for effective treatment.
Language models are few-shot learners
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Origa-light: An online retinal fundus image database for glaucoma analysis and research
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An assessment of the health and economic burdens of glaucoma
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Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
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The diabetic retinopathy barometer study: global perspectives on access to and experiences of diabetic retinopathy screening and treatment
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Prevalence of undiagnosed age-related macular degeneration in primary eye care
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Clinically applicable deep learning for diagnosis and referral in retinal disease
Jeffrey De Fauw, Joseph R Ledsam, Bernardino Romera-Paredes, Stanislav Nikolov, Nenad Tomasev, Sam Blackwell, Harry Askham, Xavier Glorot, Brendan O’Donoghue, Daniel Visentin, et al. 2018 · 2018
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Indian diabetic retinopathy image dataset (idrid)
Porwal Prasanna, Pachade Samiksha, Kamble Ravi, Kokare Manesh, D Girish, S Vivek, and Meriaudeau Fabrice. 2018 · 2018
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Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song. 2019 · 2019
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Deepseenet: a deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs
Yifan Peng, Shazia Dharssi, Qingyu Chen, Tiarnan D Keenan, Elvira Agrón, Wai T Wong, Emily Y Chew, and Zhiyong Lu. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
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Artificial intelligence and deep learning in ophthalmology
Daniel Shu Wei Ting, Louis R Pasquale, Lily Peng, John Peter Campbell, Aaron Y Lee, Rajiv Raman, Gavin Siew Wei Tan, Leopold Schmetterer, Pearse A Keane, and Tien Yin Wong. 2019 · 2019
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G1020: A benchmark retinal fundus image dataset for computer-aided glaucoma detection
Muhammad Naseer Bajwa, Gur Amrit Pal Singh, Wolfgang Neumeier, Muhammad Imran Malik, Andreas Dengel, and Sheraz Ahmed. 2020 · 2020
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International evaluation of an ai system for breast cancer screening
Scott Mayer McKinney, Marcin Sieniek, Varun Godbole, Jonathan Godwin, Natasha Antropova, Hutan Ashrafian, Trevor Back, Mary Chesus, Greg S Corrado, Ara Darzi, et al. 2020 · 2020
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Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
José Ignacio Orlando, Huazhu Fu, João Barbosa Breda, Karel Van Keer, Deepti R Bathula, Andrés Diaz-Pinto, Ruogu Fang, Pheng-Ann Heng, Jeyoung Kim, JoonHo Lee, et al. 2020 · 2020
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Green ai
Roy Schwartz, Jesse Dodge, Noah A Smith, and Oren Etzioni. 2020 · 2020
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A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability
Saad M Khan, Xiaoxuan Liu, Siddharth Nath, Edward Korot, Livia Faes, Siegfried K Wagner, Pearse A Keane, Neil J Sebire, Matthew J Burton, and Alastair K Denniston. 2021 · 2021
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Annotation-efficient deep learning for automatic medical image segmentation
Shanshan Wang, Cheng Li, Rongpin Wang, Zaiyi Liu, Meiyun Wang, Hongna Tan, Yaping Wu, Xinfeng Liu, Hui Sun, Rui Yang, et al. 2021 · 2021
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Deeplensnet: deep learning automated diagnosis and quantitative classification of cataract type and severity
Tiarnan DL Keenan, Qingyu Chen, Elvira Agrón, Yih-Chung Tham, Jocelyn Hui Lin Goh, Xiaofeng Lei, Yi Pin Ng, Yong Liu, Xinxing Xu, Ching-Yu Cheng, et al. 2022 · 2022
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Medical image understanding with pretrained vision language models: A comprehensive study
Ziyuan Qin, Hua Hui Yi, Qicheng Lao, and Kang Li. 2022 · 2022
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Gpt-4 technical report
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
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Evaluating the performance of chatgpt in ophthalmology: an analysis of its successes and shortcomings
Fares Antaki, Samir Touma, Daniel Milad, Jonathan El-Khoury, and Renaud Duval. 2023 · 2023
A foundation model for generalizable disease detection from retinal images
Yukun Zhou, Mark A Chia, Siegfried K Wagner, Murat S Ayhan, Dominic J Williamson, Robbert R Struyven, Timing Liu, Moucheng Xu, Mateo G Lozano, Peter Woodward-Court, et al. 2023 · 2023
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Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks
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Deep learning-based classification of eye diseases using convolutional neural network for oct images
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Cataract-1k dataset for deep-learning-assisted analysis of cataract surgery videos
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Language enhanced model for eye (leme): An open-source ophthalmology-specific large language model
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Large language models and their impact in ophthalmology
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The future landscape of large language models in medicine
Jan Clusmann, Fiona R Kolbinger, Hannah Sophie Muti, Zunamys I Carrero, Jan-Niklas Eckardt, Narmin Ghaffari Laleh, Chiara Maria Lavinia Löffler, Sophie-Caroline Schwarzkopf, Michaela Unger, Gregory P Veldhuizen, et al. 2023 · 2023
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Eye disease detection through image classification using federated learning
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Unk-vqa: A dataset and a probe into the abstention ability of multi-modal large models
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Ophnet: A large-scale video benchmark for ophthalmic surgical workflow understanding
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Visual instruction tuning
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Harvard glaucoma fairness: a retinal nerve disease dataset for fairness learning and fair identity normalization
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Medtrinity-25m: A large-scale multimodal dataset with multigranular annotations for medicine
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Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis
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