A complete benchmark for polyp detection, segmentation and classification in colonoscopy images - 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; et al. (Frontiers in Oncology, 2024)
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.