  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.
