2023/04/15 by Tao Zhou, Zhou, Tao, Yizhe Zhang +7 · 1 citation
Medicine · #Colorectal Cancer Screening and Detection #Colorectal Cancer Surgical Treatments #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Radiomics and Machine Learning in Medical Imaging
paper · pdf · doi:10.48550/arxiv.2304.07583
openalex publication_date 2023/04/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has demonstrated promising performance in several segmentation tasks. As we know, polyp segmentation is a fundamental task in the medical imaging field, which plays a critical role in the diagnosis and cure of colorectal cancer. In particular, applying SAM to the polyp segmentation task is interesting. In this report, we evaluate the performance of SAM in segmenting polyps, in which SAM is under unprompted settings. We hope this report will provide insights to advance this polyp segmentation field and promote more interesting works in the future. This project is publicly at https://github.com/taozh2017/SAMPolyp.