EGID: A Comprehensive Multi-label Endoscopic Image Dataset for Gastritis Classification——part 1

Chronic atrophic gastritis is a well-recognized precancerous condition, emphasizing the need for accurate endoscopic classification. However, existing endoscopic datasets lack comprehensive multi-dimensional annotations for systematic gastritis classification. We present the Endoscopic Gastritis Image Dataset (EGID), comprising 5,883 high-quality white-light endoscopic images from 229 patients retrospectively collected at Renji Hospital, Shanghai, using the Olympus CV-290 system between March and November 2024. Each patient’s images are systematically annotated across four independent clinical dimensions: H. pylori infection status, presence of gastric atrophy, distribution of atrophy, and gastritis type. Annotations were validated through independent assessment by two experienced endoscopists, with consensus review by a senior gastroenterologist for discordant cases, achieving excellent inter-rater agreement (Cohen’s Kappa: 0.947-1.000). EGID provides the first publicly available multi-label endoscopic gastritis dataset, enabling development of artificial intelligence (AI)-based classification systems and serving as an educational resource for clinical training in gastritis diagnosis and cancer risk stratification.