ArticleScientific data2025
GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Lumina-Net: temporal learning with feature fusion for endoscopic artifact removal.Medical & biological engineering & computing · 2026Article
- EGID: A Comprehensive Multi-label Endoscopic Image Dataset for Gastritis Classification.Scientific data · 2026Article
- Dual-stream token fusion with Swin Transformer and lesion-aware tokens for gastric metaplasia classification in IoMT-assisted deployment.BMC medical imaging · 2026Article
- Artificial Intelligence in Gastrointestinal Endoscopy: Current Advances, Clinical Integration, and Future Directions: A Narrative Review.Clinical and experimental gastroenterology · 2026Review
- Single-Time Gastroscopy in High-Risk Patients: Screening Effectiveness for Gastric Precancerous Conditions in a Low-To Moderate-Incidence Population.Journal of clinical medicine · 2025Article
- GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach.Scientific data · 2025Article
- Improving image-retrieval performance of foundation models in gastrointestinal endoscopic images.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Endoscopy is vital for detecting and diagnosing gastrointestinal diseases. Systematic examination protocols are key to enhancing detection, particularly for the early identification of premalignant conditions. Publicly available endoscopy image databases are crucial for machine learning research, yet challenges persist, particularly in identifying upper gastrointestinal anatomical landmarks to ensure effective and precise endoscopic procedures. However, many existing datasets have inconsistent labeling and limited accessibility, leading to biased models and reduced generalizability. This paper introduces GastroHUN, an open dataset documenting stomach screening procedures based on a systematic protocol. GastroHUN includes 8,834 images from 387 patients and 4,729 labeled video sequences, all annotated by four experts. The dataset covers 22 anatomical landmarks in the stomach and includes an additional category for unqualified images, making it a valuable resource for AI model development. By providing a robust public dataset and baseline deep learning models for image and sequence classification, GastroHUN serves as a benchmark for future research and aids in the development of more effective algorithms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.