Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge.

ABSTRACT: Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, t

SUBMITTER: Ali S 

PROVIDER: S-EPMC10805888 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature