@article{16133,
  author = {Pia Smedsrud and Vajira Thambawita and Steven Hicks and Henrik Gjestang and Oda Nedrejord and Espen N{\ae}ss and Hanna Borgli and Debesh Jha and Tor Berstad and Sigrun Eskeland and Mathias Lux and H{\r a}vard Espeland and Andreas Petlund and Duc Nguyen and Enrique Garcia-Ceja and Dag Johansen and Peter Schmidt and Ervin Toth and Hugo Hammer and Thomas de Lange and Michael Riegler and P{\r a}l Halvorsen},
  title = {Kvasir-Capsule, a video capsule endoscopy dataset},
  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 Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule 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 Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.},
  year = {2021},
  journal = {Scientific Data},
  volume = {8},
  pages = {142},
  publisher = {Springer Nature},
  url = {http://www.nature.com/articles/s41597-021-00920-z},
  doi = {10.1038/s41597-021-00920-z},
}