Physics-informed deep generative learning for quantitative assessment of the retina
E. Brown, A. Guy, N. Holroyd, P. Sweeney, L. Gourmet, H. Coleman, C. Walsh, A. Markaki, R. Shipley, R. Rajendram, S. Walker-Samuel
This paper introduces a physics-informed GAN to generate realistic retinal blood vessel models, outperforming human labeling in segmentation and reconstruction.
bioRxiv · Jul 10, 2023Biophysics