Supervised Polyp Segmentation

Supervised polyp segmentation in medical imaging aims to automatically identify and delineate polyps in colonoscopy images, aiding in early colorectal cancer detection. Current research emphasizes reducing the need for extensive manual annotations by exploring semi-supervised and weakly-supervised learning approaches, leveraging techniques like consistency training, scribble-based annotations, and box-level supervision. These methods often incorporate advanced architectures that integrate multi-scale feature extraction and contrastive learning to improve segmentation accuracy. The ultimate goal is to improve the efficiency and accuracy of polyp detection, leading to earlier diagnosis and potentially better patient outcomes.