Polyp Segmentation
Overview
Polyp segmentation studies how to outline colorectal polyps in colonoscopy images and video, helping clinicians identify lesions and assess their shape and extent. Common approaches include CNN encoder-decoder models such as U-Net, attention and transformer or state-space networks, and foundation-model features; video methods also use information from nearby frames. Current work explores boundary refinement, sparse-label learning, cross-dataset generalization, and compact models that can run in real time on clinical or edge hardware. A key tension is achieving reliable boundaries across imaging devices, centers, and occlusions while keeping evaluation comparable and inference efficient.
Latest papers
23 papers2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.