Kvasir-Capsule, a video capsule endoscopy dataset.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
27 05 2021
27 05 2021
Historique:
received:
13
08
2020
accepted:
15
04
2021
entrez:
28
5
2021
pubmed:
29
5
2021
medline:
16
10
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 34045470
doi: 10.1038/s41597-021-00920-z
pii: 10.1038/s41597-021-00920-z
pmc: PMC8160146
doi:
Types de publication
Dataset
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
142Subventions
Organisme : Norges Forskningsråd (Research Council of Norway)
ID : 282315
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