Fundus Image

Fundus images, photographs of the back of the eye, are crucial for diagnosing various retinal diseases. Current research focuses on improving the accuracy and efficiency of automated analysis using deep learning models, including convolutional neural networks (CNNs), Vision Transformers (ViTs), and generative adversarial networks (GANs), often incorporating techniques like attention mechanisms and self-supervised learning to handle noisy or limited data. These advancements aim to improve diagnostic accuracy, reduce the need for expert interpretation, and enable large-scale screening programs for early detection and intervention of eye diseases, ultimately impacting patient care and public health. A significant area of ongoing work addresses the challenges of generalizability and bias in these models to ensure equitable performance across diverse populations and imaging modalities.