GIM-ENDO: A Multimodal Endoscopic Image and Video Dataset for Gastric Intestinal Metaplasia Morphology and Pathology

Gastric intestinal metaplasia (GIM) is a histopathologically confirmed precursor to gastric adenocarcinoma. Early, accurate detection remains a clinical challenge, and AI-assisted endoscopy is a promising solution. We present GIM-ENDO, a publicly available multimodal dataset comprising 98 endoscopic still images and 39 video clips from 24 patients (22 GIM-positive, 2 normal controls), acquired with the Olympus EVIS X1 system under white-light endoscopy (WLE), narrow-band imaging (NBI), and magnifying NBI (M-NBI). All cases are histopathologically confirmed. Annotations cover six IEE endoscopic signs (LBC, MTB, WOS, TV pattern/Fusion, atrophy, MLE), GIM subtype (complete/incomplete), and OLGA/OLGIM staging where available. The dataset is publicly accessible at https://doi.org/10.5281/zenodo.20707267. For the latest updates, refer to https://databiox.com.

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Publication Details

Journal
The Journal of Machine Learning for Biomedical Imaging
Published
2026-09-21
DOI
https://doi.org/10.59275/j.melba.2026-86cc
Primary Topic
Colorectal Cancer Screening and Detection
Type
article
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article

GIM-ENDO: A Multimodal Endoscopic Image and Video Dataset for Gastric Intestinal Metaplasia Morphology and Pathology

Mohammad Tashakoripour, Mojgan Forootan, Mahziar Setayeshfar, Ali Darvishi et al.
The Journal of Machine Learning for Biomedical Imaging
Colorectal Cancer Screening and Detection
article

GIM-ENDO: A Multimodal Endoscopic Image and Video Dataset for Gastric Intestinal Metaplasia Morphology and Pathology

Mohammad Tashakoripour, Mojgan Forootan, Mahziar Setayeshfar, Ali Darvishi, Hamidreza Bolhasani
article en

Abstract

Gastric intestinal metaplasia (GIM) is a histopathologically confirmed precursor to gastric adenocarcinoma. Early, accurate detection remains a clinical challenge, and AI-assisted endoscopy is a promising solution. We present GIM-ENDO, a publicly available multimodal dataset comprising 98 endoscopic still images and 39 video clips from 24 patients (22 GIM-positive, 2 normal controls), acquired with the Olympus EVIS X1 system under white-light endoscopy (WLE), narrow-band imaging (NBI), and magnifying NBI (M-NBI). All cases are histopathologically confirmed. Annotations cover six IEE endoscopic signs (LBC, MTB, WOS, TV pattern/Fusion, atrophy, MLE), GIM subtype (complete/incomplete), and OLGA/OLGIM staging where available. The dataset is publicly accessible at https://doi.org/10.5281/zenodo.20707267. For the latest updates, refer to https://databiox.com.

The Journal of Machine Learning for Biomedical ImagingVol. 2026(MICCAI Open Data 2026)
Iran University of Medical Sciences (IR), Shiraz University of Medical Sciences (IR), Shahid Beheshti University of Medical Sciences (IR), Tehran University of Medical Sciences (IR)
Openalex Percentile: Top 14%
Colorectal Cancer Screening and Detection
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GIM-ENDO: A Multimodal Endoscopic Image and Video Dataset for Gastric Intestinal Metaplasia Morphology and Pathology — Mohammad Tashakoripour, Mojgan Forootan, et al. · The Journal of Machine Learning for Biomedical Imaging (2026) | TGRS Research Map | TGRS