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

arXiv: https://arxiv.org/abs/2307.15444 · HTML: https://mlsanity.com/paper/2307.15444/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 32 references that are in the map.
- First posted on arXiv 28 Jul 2023.

## Builds on (most cited first, 1 of 1)

- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)

## Built on by (most cited first, 2 of 2)

- [RRTS Dataset: A Benchmark Colonoscopy Dataset from Resource-Limited Settings for Computer-Aided Diagnosis Research](https://mlsanity.com/paper/2511.06769/) (2025)
- [PolypVision: A Three-Stage Hierarchical Deep Learning Framework for Classification and Segmentation of Colorectal Polyps](https://mlsanity.com/paper/2608.10649/) (2026)

## Abstract (arXiv)

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.