Automated analysis of retinal imaging using machine learning techniques for computer vision. - Jeffrey De Fauw; Pearse A. Keane; Nenad Tomasev; Daniel Visentin; George van den Driessche 002; Mike Johnson; Cían Hughes; Carlton Chu; Joseph R. Ledsam; Trevor Back; Tunde Peto; Geraint Rees 001; et al. (F1000Research, 2016)

Deep Learning and Ensemble Method for Optic Disc and Cup Segmentation. - Jongwoo Kim; Loc Q. Tran; Tunde Peto; Emily Y. Chew (CIBCB, 2022)

Shape-Aware Weakly/Semi-Supervised Optic Disc and Cup Segmentation with Regional/Marginal Consistency. - Yanda Meng; Xu Chen 030; Hongrun Zhang; Yitian Zhao; Dongxu Gao; Barbra Hamill; Godhuli Patri; Tunde Peto; Savita Madhusudhan; Yalin Zheng (MICCAI (4), 2022)

Dual Consistency Enabled Weakly and Semi-Supervised Optic Disc and Cup Segmentation With Dual Adaptive Graph Convolutional Networks. - Yanda Meng; Hongrun Zhang; Yitian Zhao; Dongxu Gao; Barbra Hamill; Godhuli Patri; Tunde Peto; Savita Madhusudhan; Yalin Zheng (IEEE Trans. Medical Imaging, 2023)

Acceptability of Home Monitoring for Neovascular Age-Related Macular Degeneration Reactivation: A Qualitative Study. - Sean O'Connor; Charlene Treanor; Elizabeth Ward; Robin Wickens; Abby O'Connell; Lucy Culliford; Chris Rogers; Eleanor Gidman; Tunde Peto; Paul Knox; Benjamin Burton; Andrew J. Lotery; et al. (Nursing Informatics, 2024)

Multi-granularity learning of explicit geometric constraint and contrast for label-efficient medical image segmentation and differentiable clinical function assessment. - Yanda Meng; Yuchen Zhang; Jianyang Xie; Jinming Duan 001; Martha Joddrell; Savita Madhusudhan; Tunde Peto; Yitian Zhao; Yalin Zheng (Medical Image Anal., 2024)

CACTUS as a Reliable Tool for Early Classification of Age-related Macular Degeneration. - Luca Gherardini; Imre Lengyel; Tunde Peto; Caroline C. W. Klaver; Magda A. Meester-Smoord; Johanna M. Colijn; EYE-RISK Consortium; E3 Consortium; Jose Sousa (CoRR, 2025)