2026-04-14T03:05:11Zhttps://recercat.cat/oai/request
oai:recercat.cat:2072/4881882025-10-27T13:56:47Zcom_2072_98col_2072_378192
RECERCAT author Tudela, Yael author Majó, Mireia author de la Fuente, Neil author Galdran, Adrian author Krenzer, Adrian author Puppe, Frank author Yamlahi, Amine author Tran, Thuy Nuong author Matuszewski, Bogdan J. author Fitzgerald, Kerr author Bian, Cheng author Pan, Junwen author Liu, Shijle author Fernández-Esparrach, Gloria author Histace, Aymeric author Bernal del Nozal, Jorge 2025-10-27T13:56:46Z 2025-10-27T13:56:46Z 2024 http://hdl.handle.net/2072/488188 Colorectal cancer (CRC) is one of the main causes of deaths worldwide. Early detection and diagnosis of its precursor lesion, the polyp, is key to reduce its mortality and to improve procedure efficiency. During the last two decades, several computational methods have been proposed to assist clinicians in detection, segmentation and classification tasks but the lack of a common public validation framework makes it difficult to determine which of them is ready to be deployed in the exploration room. This study presents a complete validation framework and we compare several methodologies for each of the polyp characterization tasks. Results show that the majority of the approaches are able to provide good performance for the detection and segmentation task, but that there is room for improvement regarding polyp classification. While studied show promising results in the assistance of polyp detection and segmentation tasks, further research should be done in classification task to obtain reliable results to assist the clinicians during the procedure. The presented framework provides a standarized method for evaluating and comparing different approaches, which could facilitate the identification of clinically prepared assisting methods. open access Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. https://creativecommons.org/licenses/by/4.0/ Computer-aided diagnosis Medical imaging Polyp classification Polyp detection Polyp segmentation A complete benchmark for polyp detection, segmentation and classification in colonoscopy images Article