公开首个融合内镜与病理的胃肠化生数据集，助力AI早筛癌症前病变
GIM-ENDO: A Multimodal Endoscopic Image and Video Dataset for Gastric Intestinal Metaplasia Morphology and Pathology
- 整合24例患者多模态内镜图像与病理结果，标注6种关键征象
- 包含完整/不完整肠化分型及OLGIM分期，支持精准模型训练
- 适合胃癌筛查AI研究者使用，推动早期诊断技术落地
胃肠化生（GIM）是胃癌前病变的重要阶段，早期发现对阻断癌变进程至关重要。人工智能在实时内镜检测和表征GIM方面具有巨大潜力，但受限于缺乏公开、经组织学验证的数据集，尤其缺少结合详细内镜标注、组织学亚型（完全型与不完全型）、标准化分期系统及正常黏膜模式的数据。为此，我们构建了GIM-ENDO数据集，涵盖24例患者（22例阳性，2例正常对照）的临床信息、内镜表现、组织病理结果及幽门螺杆菌状态。使用欧林巴斯EVIS X1系统采集白光内镜（WLE）与增强内镜（IEE），包括窄带成像（NBI）与放大NBI（M-NBI），并提供图像与视频片段。标注内容包括六种主要的IEE征象：淡蓝色脊（LBC）、边缘浑浊带（MTB）、白色透明物质（WOS）、TV模式（融合型）、萎缩及地图样红斑（MLE），以及出现时记录的两种附加征象（AHP与GA）。所有阳性病例均标注肠化亚型，部分病例提供完整的OLGA与OLGIM分期。该数据集已通过https://doi.org/10.5281/zenodo.20707267 公开，最新信息请访问DataBioX官网：https://databiox.com。本工作简版已提交至MICCAI 2026开放数据赛道。
原文摘要 · Abstract (English)
Gastric intestinal metaplasia (GIM) is a precursor lesion to gastric dysplasia and adenocarcinoma whose early detection is crucial for intervening in the carcinogenesis cascade. Artificial intelligence (AI) holds considerable promise for real-time endoscopic detection and characterization of GIM. However, development of reliable AI models has been constrained by the absence of publicly available, histopathologically validated datasets that combine detailed endoscopic annotations, histological subtype (complete and incomplete), standardized grading systems, and normal mucosal patterns. GIM-ENDO was designed to fill this gap. The dataset comprises demographic data, endoscopic findings, histopathological results, and H. pylori status acquired using the Olympus EVIS X1 system with white-light endoscopy (WLE) and image-enhanced endoscopy (IEE), including narrow-band imaging (NBI) and magnifying NBI (M-NBI), along with images and video clips from 24 patients (22 GIM-positive, 2 normal controls). Annotations cover six primary IEE endoscopic signs -- light blue crest (LBC), marginal turbid band (MTB), white opaque substance (WOS), TV pattern (Fusion), atrophy, and map-like erythema (MLE) -- plus two additional endoscopic findings (AHP and GA) recorded where present. GIM subtypes (complete and incomplete) are annotated for all GIM-positive cases; OLGA and OLGIM staging are provided where complete histological sampling was available. The dataset is publicly accessible at https://doi.org/10.5281/zenodo.20707267. For the latest updates and further information regarding this dataset, readers are referred to the DataBioX website: https://databiox.com A short version of this work has been submitted to MICCAI 2026 Open Data Track.
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