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HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.
Hanna Borgli1,2, Vajira Thambawita1,3, Pia H Smedsrud1,2,4
1SimulaMet, Oslo, Norway.
Scientific Data
|August 30, 2020
Summary
This study introduces HyperKvasir, a large dataset for gastrointestinal tract analysis. This resource aids in developing artificial intelligence (AI) tools for medical image analysis and computer-assisted diagnosis.
Area of Science:
- Medical imaging
- Artificial intelligence in medicine
- Gastroenterology
Background:
- Medical data scarcity and labeling challenges hinder AI development.
- Automatic analysis systems for disease detection are difficult to create.
- Need for comprehensive datasets in medical AI research.
Purpose of the Study:
- To present HyperKvasir, the largest available dataset of gastrointestinal tract images and videos.
- To facilitate the development of AI-powered diagnostic tools for endoscopy.
- To support advancements in computer-assisted medical examination systems.
Main Methods:
- Dataset compilation from real gastro- and colonoscopy examinations.
- Inclusion of 110,079 images and 374 videos, totaling approximately 1 million frames.
- Data labeled by experienced gastrointestinal endoscopists, including anatomical landmarks and findings.
Main Results:
- The HyperKvasir dataset is the largest of its kind for the gastrointestinal tract.
- Initial experiments show the potential of AI for computer-assisted diagnosis.
- The dataset encompasses diverse findings, from normal to pathological.
Conclusions:
- The HyperKvasir dataset is a valuable resource for advancing AI in medical diagnostics.
- It can significantly contribute to developing improved algorithms for gastrointestinal endoscopy.
- The dataset's potential applications extend to other medical fields requiring image analysis.
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