ColonOOD: A complete pipeline for optical diagnosis of colorectal polyps integrating out-of-distribution detection and uncertainty quantification

Abstract

The rising prevalence of colorectal cancer necessitates early and accurate optical diagnosis of colorectal polyps. Despite advances in Computer-Aided Diagnosis (CAD) systems, challenges like data variability and inconsistent clinical performance hinder their widespread use. To address these limitations, we propose ColonOOD, an integrated CAD system for polyp localization, uncertainty-aware polyp classification, and Out-of-Distribution (OOD) polyp detection during colonoscopy. ColonOOD ensures robust classification of adenomatous, hyperplastic, and OOD polyps while providing calibrated uncertainty scores to support clinical decisions. Extensive evaluations across four medical centers and two public datasets demonstrate ColonOOD's strong performance, achieving up to 79.69% classification and 75.53 % OOD detection accuracy. This system offers reliable insights for endoscopists, marking a significant step toward broader clinical adoption of automated diagnostic tools in colorectal cancer care.