@Article{Borgli2020, author = {Hanna Borgli and Vajira Thambawita and Pia H. Smedsrud and Steven Hicks and Debesh Jha and Sigrun L. Eskeland and Kristin Ranheim Randel and Konstantin Pogorelov and Mathias Lux and Duc Tien Dang Nguyen and Dag Johansen and Carsten Griwodz and H{\aa}kon K. Stensland and Enrique Garcia-Ceja and Peter T. Schmidt and Hugo L. Hammer and Michael A. Riegler and Paal Halvorsen and Thomas de Lange}, journal = {Scientific Data}, title = {{HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy}}, year = {2020}, issn = {2052-4463}, month = {aug}, number = {1}, volume = {7}, abstract = {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.}, doi = {10.1038/s41597-020-00622-y}, keywords = {Statistics, Probability and Uncertainty, Statistics and Probability, Education, Library and Information Sciences, Information Systems, Computer Science Applications}, publisher = {Springer Science and Business Media LLC}, url = {https://www.nature.com/articles/s41597-020-00622-y} }