ColonOOD: A Complete Pipeline for Optical Diagnosis of Colorectal Polyps Integrating Out‑of‑Distribution Detection and Uncertainty Quantification
2024.04 - 2025.06
[Key Concept]
Colorectal cancer diagnosis demands accurate and reliable polyp assessment during colonoscopy. ColonOOD is an integrated system for polyp localization, uncertainty-aware classification, and Out-of-Distribution (OOD) detection. By handling both familiar and novel polyp types with calibrated confidence, it improves clinical reliability. Validated across multiple hospitals and datasets, ColonOOD brings CAD systems closer to real-world adoption.
Research Goal and the target sceanrio.
The overview of the proposed framework named ColonOOD.
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.
@inproceedings{colonood,author={Park*, Sehyun and Lee*, Dongheon and Lee, Ji Young and Chun, Jaeyoung and Chang, Ji Young and Baek, Eunsu and Jin, Eun Hyo and Kim, Hyung‑Sin},title={ColonOOD: A Complete Pipeline for Optical Diagnosis of Colorectal Polyps Integrating Out‑of‑Distribution Detection and
Uncertainty Quantification},booktitle={Expert Systems with Applications, Volume 295, 128756},year={2026},month=jan,tags={colonood}}
