2024/08/12 by Mobina Mansoori, Sajjad Shahabodini, Mansoori, Mobina +7 · 3 citations
Medicine · #Artificial intelligence #Cancer #Colonoscopy #Colorectal Cancer Screening and Detection #Colorectal Polyp #Colorectal and Anal Carcinomas #Colorectal cancer #Computer Vision and Pattern Recognition (cs.CV) #Computer science #FOS: Computer and information sciences #FOS: Electrical engineering #Gastric Cancer Management and Outcomes #Image and Video Processing (eess.IV) #Internal medicine #Machine Learning (cs.LG) #Materials science #Medicine #Philosophy #Segmentation #Shot (pellet) #Zero (linguistics) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2408.05892
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/08/12 · openalex created_date 2024/10/21 · openalex updated_date 2026/07/28
Polyp segmentation plays a crucial role in the early detection and diagnosis of colorectal cancer. However, obtaining accurate segmentations often requires labor-intensive annotations and specialized models. Recently, Meta AI Research released a general Segment Anything Model 2 (SAM 2), which has demonstrated promising performance in several segmentation tasks. In this manuscript, we evaluate the performance of SAM 2 in segmenting polyps under various prompted settings. We hope this report will provide insights to advance the field of polyp segmentation and promote more interesting work in the future. This project is publicly available at https://github.com/ sajjad-sh33/Polyp-SAM-2.