Generative Adversarial Networks in Ophthalmology

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Identifier Generative_Adversarial_Networks_in_Ophthalmology
Title Generative Adversarial Networks in Ophthalmology
Creator Babajide Olubusayo Owosela; Anjolaoluwa Popoola; Sachin Kedar
Affiliation (BOO) Department of Ophthalmology, Emory University; (AP) H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology; (SK) Department of Ophthalmology, Emory University
Subject Artificial Intelligence; Generative Adversarial Networks; Machine Learning; Synthetic Data
Description In this video we will describe Generative Adversarial Networks (GAN) models and its applications and limitations in Ophthalmology. GAN, a technique within machine learning, enables computers to utilize real data to generate valuable synthetic data, artificially produced information that mimics the statistical properties of real data, that can be used by health professionals and researchers in ophthalmic clinical practice, research, and medical education.
Date 2024-03
References Arora A. (2020). Artificial intelligence: a new frontier for anaesthesiology training. British journal of anaesthesia, 125(5), e407-e408. https://doi.org/10.1016/j.bja.2020.06.049. 2. Arora, A., & Arora, A. (2022). Generative adversarial networks and synthetic patient data: current challenges and future perspectives. Future healthcare journal, 9(2), 190-193. Presentations are communication tools that can be used as demonstrations, lectures, speeches, reports, and more. 3. Burlina, P. M., Joshi, N., Pacheco, K. D., Liu, T. Y. A., & Bressler, N. M. (2019). Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration. JAMA ophthalmology, 137(3), 258-264. https://doi.org/10.1001/jamaophthalmol.2018.6156. 4. Semerád, L., Drahanský, M. (2020). Retinal Vascular Characteristics. In: Uhl, A., Busch, C., Marcel, S., Veldhuis, R. (eds) Handbook of Vascular Biometrics. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-27731-4_11. 5. Wang, Z., Lim, G., Ng, W. Y., Keane, P. A., Campbell, J. P., Tan, G. S. W., Schmetterer, L., Wong, T. Y., Liu, Y., & Ting, D. S. W. (2021). Generative adversarial networks in ophthalmology: what are these and how can they be used?. Current opinion in ophthalmology, 32(5), 459-467. Presentations are communication tools that can be used as demonstrations, lectures, speeches, reports, and more. 6. Wu, Y., Donahue, J., Balduzzi, D., Simonyan, K., & Lillicrap, T. (2019). LOGAN: Latent Optimisation for Generative Adversarial Networks (Version 2). arXiv. https://doi.org/10.48550/ARXIV.1912.00953. 7. You, A., Kim, J. K., Ryu, I. H., & Yoo, T. K. (2022). Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey. Eye and vision (London, England), 9(1), 6. Presentations are communication tools that can be used as demonstrations, lectures, speeches, reports, and more. 8. Zhu, T., Li, K., Herrero, P., & Georgiou, P. (2023). GluGAN: Generating Personalized Glucose Time Series Using Generative Adversarial Networks. IEEE journal of biomedical and health informatics, 27(10), 5122-5133. https://doi.org/10.1109/JBHI.2023.3271615
Language eng
Format video/mp4
Type Image/MovingImage
Collection Neuro-Ophthalmology Virtual Education Library: NOVEL https://NOVEL.utah.edu
Publisher North American Neuro-Ophthalmology Society
Holding Institution Spencer S. Eccles Health Sciences Library, University of Utah
Rights Management Copyright 2024. For further information regarding the rights to this collection, please visit: https://NOVEL.utah.edu/about/copyright
ARK ark:/87278/s6nshzwq
Setname ehsl_novel_novel
ID 2456808
Reference URL https://collections.lib.utah.edu/ark:/87278/s6nshzwq
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