Retinal Imaging

Retinal imaging uses advanced imaging techniques to capture detailed images of the retina, aiming to improve the diagnosis and management of various eye diseases and even systemic conditions like dementia and cardiovascular disease. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based models like Vision Transformers, often incorporating techniques like self-supervised learning, transfer learning, and multimodal fusion to enhance accuracy and generalizability across different imaging modalities and datasets. These advancements hold significant promise for improving diagnostic accuracy, enabling earlier disease detection, and potentially reducing the burden on healthcare systems through automated analysis and improved accessibility of screening.