Stable Short Term Polyp Representation

Stable short-term polyp representation in colonoscopy video aims to create robust and accurate computer-aided detection (CAD) systems for identifying and characterizing colorectal polyps. Current research focuses on developing deep learning models, including variations of UNet, Transformer, and recurrent neural networks (like ConvLSTM), often incorporating techniques like attention mechanisms and feature fusion to improve polyp segmentation and classification accuracy, even in challenging scenarios with low-quality frames or motion artifacts. These advancements are crucial for improving the efficiency and accuracy of polyp detection during colonoscopy, leading to earlier diagnosis and treatment of colorectal cancer. The ultimate goal is to create reliable, real-time CAD systems that assist clinicians in identifying and characterizing polyps, improving patient outcomes.