Polyp Segmentation Task

Polyp segmentation, the automated identification and delineation of polyps in medical images (primarily colonoscopy), aims to improve the accuracy and efficiency of colorectal cancer diagnosis. Current research heavily utilizes deep learning, employing architectures like U-Net variations, DeepLabV3++, EfficientNet-based networks, and adaptations of the Segment Anything Model (SAM), often incorporating multi-scale feature extraction and attention mechanisms to address challenges posed by varying polyp sizes, shapes, and appearances. These advancements are crucial for improving the speed and accuracy of polyp detection, leading to earlier diagnosis and potentially saving lives. Furthermore, research focuses on improving model robustness and generalizability across different datasets and imaging centers, often employing techniques like transfer learning and active learning to reduce annotation costs.