AUTHOR=Tudela Yael , Majó Mireia , de la Fuente Neil , Galdran Adrian , Krenzer Adrian , Puppe Frank , Yamlahi Amine , Tran Thuy Nuong , Matuszewski Bogdan J. , Fitzgerald Kerr , Bian Cheng , Pan Junwen , Liu Shijle , Fernández-Esparrach Gloria , Histace Aymeric , Bernal Jorge 

TITLE=A complete benchmark for polyp detection, segmentation and classification in colonoscopy images

JOURNAL=Frontiers in Oncology

VOLUME=Volume 14 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1417862

DOI=10.3389/fonc.2024.1417862

ISSN=2234-943X

ABSTRACT=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.In this paper we present 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.