Polyp Segmentation

Polyp segmentation in colonoscopy images aims to automatically identify and delineate polyps, crucial for early colorectal cancer detection and treatment. Current research heavily utilizes deep learning, focusing on architectures like Vision Transformers and convolutional neural networks (CNNs), often incorporating multi-scale feature extraction, attention mechanisms, and innovative loss functions to improve segmentation accuracy, particularly at polyp boundaries. These advancements hold significant promise for improving the efficiency and accuracy of polyp detection, potentially reducing diagnostic errors and improving patient outcomes. The field is also actively exploring methods to address data scarcity and improve model generalizability across different imaging modalities and clinical settings.