Authors
Alda João Andrade, Mónica Martins, André Ferreira, Tarcísio Araújo, Luís Lopes, Victor Alves
Published in
Scientific data. Jul 04, 2026. Epub Jul 04, 2026.
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. To help address these limitations, 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 sections 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 gastroenterologists 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.
PMID:
42401597
Bibliographic data and abstract were imported from PubMed on 05 Jul 2026.
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