Cite
Generalizability of Deep Neural Networks for Vertical Cup-to-Disc Ratio Estimation in Ultra-Widefield and Smartphone-Based Fundus Images.
MLA
Yap, Boon Peng, et al. “Generalizability of Deep Neural Networks for Vertical Cup-to-Disc Ratio Estimation in Ultra-Widefield and Smartphone-Based Fundus Images.” Translational Vision Science & Technology, vol. 13, no. 4, Apr. 2024, p. 6. EBSCOhost, https://doi.org/10.1167/tvst.13.4.6.
APA
Yap, B. P., Kelvin, L. Z., Toh, E. Q., Low, K. Y., Rani, S. K., Goh, E. J. H., Hui, V. Y. C., Ng, B. K., & Lim, T. H. (2024). Generalizability of Deep Neural Networks for Vertical Cup-to-Disc Ratio Estimation in Ultra-Widefield and Smartphone-Based Fundus Images. Translational Vision Science & Technology, 13(4), 6. https://doi.org/10.1167/tvst.13.4.6
Chicago
Yap, Boon Peng, Li Zhenghao Kelvin, En Qi Toh, Kok Yao Low, Sumaya Khan Rani, Eunice Jin Hui Goh, Vivien Yip Cherng Hui, Beng Koon Ng, and Tock Han Lim. 2024. “Generalizability of Deep Neural Networks for Vertical Cup-to-Disc Ratio Estimation in Ultra-Widefield and Smartphone-Based Fundus Images.” Translational Vision Science & Technology 13 (4): 6. doi:10.1167/tvst.13.4.6.