ERCPMP: An Endoscopic Image and Video Dataset for Colorectal Polyps Morphology and Pathology

TL;DR

This paper introduces ERCPMP, a comprehensive dataset of endoscopic images, videos, and associated clinical data for colorectal polyps, aimed at advancing AI-based diagnosis and classification of polyps.

Contribution

The paper presents a new, publicly available dataset with detailed morphological and pathological annotations for colorectal polyps, supporting AI research in medical diagnosis.

Findings

Dataset includes images, videos, and clinical data of 191 patients.

Annotations based on international gastroenterology classifications.

Available on Mendeley Dataverse for research use.

Abstract

In the recent years, artificial intelligence (AI) and its leading subtypes, machine learning (ML) and deep learning (DL) and their applications are spreading very fast in various aspects such as medicine. Today the most important challenge of developing accurate algorithms for medical prediction, detection, diagnosis, treatment and prognosis is data. ERCPMP is an Endoscopic Image and Video Dataset for Recognition of Colorectal Polyps Morphology and Pathology. This dataset contains demographic, morphological and pathological data, endoscopic images and videos of 191 patients with colorectal polyps. Morphological data is included based on the latest international gastroenterology classification references such as Paris, Pit and JNET classification. Pathological data includes the diagnosis of the polyps including Tubular, Villous, Tubulovillous, Hyperplastic, Serrated, Inflammatory and Adenocarcinoma with Dysplasia Grade & Differentiation. The current version of this dataset is published and available on Elsevier Mendeley Dataverse and since it is under development, the latest version is accessible via: https://databiox.com.