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GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach
Diego Bravo1,2, Juan Frias3,4, Felipe Vera3,4
1Universidad Nacional de Colombia, Bogotá, 1100111, Colombia. dbravoh@unal.edu.co.
Scientific Data
|January 17, 2025
Summary
GastroHUN is a new open dataset for stomach screening, featuring 8,834 images and 4,729 labeled sequences. This resource aids AI development for improved gastrointestinal disease detection using endoscopy.
Area of Science:
- Medical imaging and diagnostics
- Artificial intelligence in healthcare
- Gastroenterology research
Background:
- Endoscopy is crucial for gastrointestinal disease diagnosis, with systematic protocols enhancing early detection of premalignant conditions.
- Existing endoscopy image datasets face challenges in labeling consistency and accessibility, hindering AI model development and generalizability.
- Accurate identification of upper gastrointestinal anatomical landmarks is essential for precise endoscopic procedures.
Purpose of the Study:
- To introduce GastroHUN, a novel, open-access dataset for stomach screening endoscopy.
- To provide a comprehensive resource for developing and benchmarking AI models for gastrointestinal endoscopy.
- To address limitations in existing datasets regarding labeling consistency and anatomical landmark annotation.
Main Methods:
- Development of GastroHUN dataset comprising 8,834 images and 4,729 labeled video sequences from 387 patients.
- Annotation of the dataset by four experts, covering 22 stomach anatomical landmarks and including unqualified images.
- Establishment of baseline deep learning models for image and sequence classification using the GastroHUN dataset.
Main Results:
- GastroHUN dataset offers a large-scale, systematically collected resource for AI in endoscopy.
- The dataset includes detailed annotations of 22 anatomical landmarks, improving precision in AI model training.
- Baseline deep learning models demonstrate the utility of GastroHUN for AI development in gastrointestinal imaging.
Conclusions:
- GastroHUN provides a valuable, open-access benchmark for AI research in upper gastrointestinal endoscopy.
- The dataset facilitates the development of more accurate and generalizable AI algorithms for detecting gastrointestinal diseases.
- Standardized protocols and expert annotations in GastroHUN enhance its utility for advancing AI-driven endoscopic diagnostics.
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