@article {27867,
	title = {Kvasir-Capsule, a video capsule endoscopy dataset},
	journal = {Scientific Data},
	volume = {8},
	year = {2021},
	pages = {142},
	publisher = {Springer Nature},
	abstract = {Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present\&nbsp;Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital.\&nbsp;Kvasir-Capsule\&nbsp;consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The\&nbsp;Kvasir-Capsule\&nbsp;dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.},
	doi = {10.1038/s41597-021-00920-z},
	url = {http://www.nature.com/articles/s41597-021-00920-z},
	author = {Pia H. {Smedsrud} and Thambawita, Vajira and Hicks, Steven and Gjestang, Henrik and Oda Olsen {Nedrejord} and N{\ae}ss, Espen and Borgli, Hanna and Jha, Debesh and Tor Jan Derek {Berstad} and Sigrun L. {Eskeland} and Lux, Mathias and Espeland, H{\r a}vard and Petlund, Andreas and Duc Tien Dang {Nguyen} and Garcia-Ceja, Enrique and Johansen, Dag and Peter T. {Schmidt} and Toth, Ervin and Hugo Lewi {Hammer} and de Lange, Thomas and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {27472,
	title = {HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy},
	journal = {Scientific Data},
	volume = {7},
	year = {2020},
	month = {08/2020},
	pages = {1-14},
	publisher = {Springer Nature},
	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\&nbsp;HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at B{\ae}rum 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\&nbsp;HyperKvasir\&nbsp;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.},
	keywords = {dataset, GI, Machine learning},
	doi = {10.1038/s41597-020-00622-y},
	url = {http://www.nature.com/articles/s41597-020-00622-y},
	author = {Borgli, Hanna and Thambawita, Vajira and Pia H. {Smedsrud} and Hicks, Steven and Jha, Debesh and Sigrun L. {Eskeland} and Kristin Ranheim {Randel} and Pogorelov, Konstantin and Lux, Mathias and Duc Tien Dang {Nguyen} and Johansen, Dag and Griwodz, Carsten and H{\r a}kon Kvale {Stensland} and Garcia-Ceja, Enrique and Peter T. {Schmidt} and Hugo Lewi {Hammer} and Riegler, Michael and Halvorsen, P{\r a}l and de Lange, Thomas}
}
