Retinal Image Datasets

Retinal image datasets are crucial for developing and evaluating computer vision algorithms to automate the diagnosis and monitoring of eye diseases. Current research focuses on improving the quality and diversity of these datasets, including the development of multimodal datasets incorporating various imaging modalities (e.g., fundus photography, OCT) and the use of techniques like data augmentation and synthetic image generation to address data scarcity and annotation challenges. Prominent model architectures employed include convolutional neural networks (CNNs), transformers, and generative adversarial networks (GANs), often combined with techniques like attention mechanisms and self-supervised learning to enhance performance and robustness. These advancements hold significant promise for improving the accuracy and efficiency of ophthalmic diagnosis, potentially leading to earlier detection and better management of vision-threatening conditions.