HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy
By Hanna Borgli, Vajira Lasantha Thambawita, Pia Helen Smedsrud, Steven Hicks, Debesh Jha, S. L. Eskeland, K. Randel, Konstantin Pogorelov, M. Lux, Duc Tien Dang Nguyen and 9 more. Scientific Data, 2019
566 citations recorded by the source corpus.
Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article presents HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at Bærum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos, and represents anatomical landmarks as well as pathological and normal findings. The total number of images and video frames together is around 1 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer-assisted diagnosis systems. The HyperKvasir dataset can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine. Measurement(s) lumen of digestive tract • lumen of colon Technology Type(s) Gastrointestinal Endoscopy • Colonoscopy Sample Characteristic - Organism Homo sapiens Measurement(s) lumen of digestive tract • lumen of colon Technology Type(s) Gastrointestinal Endoscopy • Colonoscopy Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12759833
Subfields: Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/131329a5142fad8e0efa475d017b2a7a8c1f297a
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In Artificial Intelligence
- Ozanan R. Meireles
- Lena Maier-Hein
- Pål Halvorsen
- Daniel Richard Leff
- Nassir Navab
- Gregory Hager
- Russell H. Taylor
- Raphael Sznitman
In Oncology
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- On evaluation metrics for medical applications of artificial intelligence (2021, background): “If this is not possible, other alternatives, like performing additional evaluation on public datasets, such as Kvasir 22 or the Sun database 23 , should be considered.”
- Surgical data science – from concepts toward clinical translation (2020, background): “A uthor M anuscript A uthor M anuscript A uthor M anuscript A uthor M anuscript Source Procedure(s)/ Activity(ies) Data Source Data Type Reference/ Annotation Year EndoSLAM (Ozyoruk et al., 2021) standard/capsule endoscopy ex-vivo/ synthetic, porcine/ phantom images 6 DoF pose, 3D map ground truth 2”