Video Polyp Segmentation

Video polyp segmentation aims to automatically identify and delineate polyps in colonoscopy videos, aiding in early colorectal cancer detection. Current research focuses on developing deep learning models, including transformer-based architectures and those incorporating temporal information through mechanisms like ConvLSTMs and attention modules, to improve segmentation accuracy and efficiency, particularly in handling challenging scenarios like motion blur and low-quality frames. These advancements are crucial for improving the accuracy and speed of polyp detection, potentially reducing missed diagnoses and improving patient outcomes. The development of large, publicly available datasets is also a significant focus, enabling more robust model training and evaluation.