TransRUPNet, a transformer-based encoder-decoder architecture, achieves accurate and real-time polyp segmentation with strong generalization capabilities across diverse colonoscopy datasets.
A novel self-prompting polyp segmentation model that integrates the YOLOv8 object detection and SAM 2 segmentation models to achieve high accuracy and efficiency with reduced annotation effort.