Retinal Lesion Segmentation

Retinal lesion segmentation aims to automatically identify and delineate diseased areas within retinal images, aiding in the diagnosis and monitoring of ophthalmic diseases like diabetic retinopathy and macular degeneration. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) such as U-Net architectures, often incorporating attention mechanisms and advanced techniques like deformable convolutions, diffusion models, and contrastive learning to improve accuracy and generalization across diverse datasets and imaging modalities. These advancements are crucial for improving the efficiency and accuracy of disease diagnosis, potentially leading to earlier interventions and better patient outcomes.