# REAL-Colon: A dataset for developing real-world AI applications in colonoscopy

arXiv: https://arxiv.org/abs/2403.02163 · HTML: https://mlsanity.com/paper/2403.02163/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [A multi-centre polyp detection and segmentation dataset for generalisability assessment](https://mlsanity.com/paper/2106.04463/) (2021)
- [Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations](https://mlsanity.com/paper/2104.10824/) (2021)
- [Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge](https://mlsanity.com/paper/2202.12031/) (2022)
- [Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation](https://mlsanity.com/paper/2012.15244/) (2020)

## Built on by (most cited first, 20 of 20)

- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024)
- [SurgViVQA: Temporally-Grounded Video Question Answering for Surgical Scene Understanding](https://mlsanity.com/paper/2511.03325/) (2025)
- [Hard-Attention Gates with Gradient Routing for Endoscopic Image Computing](https://mlsanity.com/paper/2407.04400/) (2024)
- [PolypSegTrack: Unified Foundation Model for Colonoscopy Video Analysis](https://mlsanity.com/paper/2503.24108/) (2025)
- [MedHorizon: Towards Long-context Medical Video Understanding in the Wild](https://mlsanity.com/paper/2605.06537/) (2026)
- [Towards Polyp Counting In Full-Procedure Colonoscopy Videos](https://mlsanity.com/paper/2502.10054/) (2025)
- [A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation](https://mlsanity.com/paper/2502.03430/) (2025)
- [Temporally-Aware Supervised Contrastive Learning for Polyp Counting in Colonoscopy](https://mlsanity.com/paper/2507.02493/) (2025)
- [RRTS Dataset: A Benchmark Colonoscopy Dataset from Resource-Limited Settings for Computer-Aided Diagnosis Research](https://mlsanity.com/paper/2511.06769/) (2025)
- [Colon-Bench: An Agentic Workflow for Scalable Dense Lesion Annotation in Full-Procedure Colonoscopy Videos](https://mlsanity.com/paper/2603.25645/) (2026)
- [SAGE: An Expert-Annotated South Asian GI Endoscopy Dataset for Multimodal Learning and Hallucination Analysis](https://mlsanity.com/paper/2606.22144/) (2026)
- [From Scope to Script: An Automated Report Generation Model for Gastrointestinal Endoscopy](https://mlsanity.com/paper/2510.03543/) (2025)
- [C3VDReg: A Benchmark for Local-to-Local Colonoscopic Registration toward Anatomical Localization](https://mlsanity.com/paper/2511.00260/) (2025)
- [TemporalDoRA: Temporal PEFT for Robust Surgical Video Question Answering](https://mlsanity.com/paper/2603.09696/) (2026)
- [Contrastive Learning under Noisy Temporal Self-Supervision for Colonoscopy Videos](https://mlsanity.com/paper/2605.12320/) (2026)
- [Understanding Model Behavior in Monocular Polyp Sizing](https://mlsanity.com/paper/2605.20461/) (2026)
- [TRUE-Colon: Exposing a Consistent Transfer Asymmetry in Real-Time Polyp Detection](https://mlsanity.com/paper/2608.13711/) (2026)
- [woma: a real-time foundation model and its fine-tuned models for endoscopy](https://mlsanity.com/paper/2609.15130/) (2026)
- [Lesion-centered 3D mapping of colonoscopy procedures: validation of a hierarchical ensemble pipeline on public benchmark videos](https://mlsanity.com/paper/2609.16672/) (2026)

## Abstract (arXiv)

Detection and diagnosis of colon polyps are key to preventing colorectal cancer. Recent evidence suggests that AI-based computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems can enhance endoscopists' performance and boost colonoscopy effectiveness. However, most available public datasets primarily consist of still images or video clips, often at a down-sampled resolution, and do not accurately represent real-world colonoscopy procedures. We introduce the REAL-Colon (Real-world multi-center Endoscopy Annotated video Library) dataset: a compilation of 2.7M native video frames from sixty full-resolution, real-world colonoscopy recordings across multiple centers. The dataset contains 350k bounding-box annotations, each created under the supervision of expert gastroenterologists. Comprehensive patient clinical data, colonoscopy acquisition information, and polyp histopathological information are also included in each video. With its unprecedented size, quality, and heterogeneity, the REAL-Colon dataset is a unique resource for researchers and developers aiming to advance AI research in colonoscopy. Its openness and transparency facilitate rigorous and reproducible research, fostering the development and benchmarking of more accurate and reliable colonoscopy-related algorithms and models.