Curated endoscopic retrograde cholangiopancreatography images dataset

TL;DR

This paper introduces a large, curated dataset of ERCP images, including labeled and reviewed images, to facilitate AI-driven diagnosis of biliary and pancreatic diseases, addressing the scarcity of public datasets.

Contribution

It provides a comprehensive, manually annotated ERCP image dataset from expert gastroenterologists, supporting future AI research and benchmarking in this medical domain.

Findings

Dataset contains 19,018 raw images and 19,317 processed images.

5,519 images are labeled for diagnostic use.

Validation through classification experiments demonstrates dataset utility.

Abstract

Endoscopic Retrograde Cholangiopancreatography (ERCP) is a key procedure in the diagnosis and treatment of biliary and pancreatic diseases. Artificial intelligence has been pointed as one solution to automatize diagnosis. However, public ERCP datasets are scarce, which limits the use of such approach. Therefore, this study aims to help fill this gap by providing a large and curated dataset. The collection is composed of 19.018 raw images and 19.317 processed from 1.602 patients. 5.519 images are labeled, which provides a ready to use dataset. All images were manually inspected and annotated by two gastroenterologist with more than 5 years of experience and reviewed by another gastroenterologist with more than 20 years of experience, all with more than 400 ERCP procedures annually. The utility and validity of the dataset is proven by a classification experiment. This collection aims to provide or contribute for a benchmark in automatic ERCP analysis and diagnosis of biliary and pancreatic diseases.