Kvasir-Capsule, a video capsule endoscopy dataset-Reference-Cited by-同舟云学术

Kvasir-Capsule, a video capsule endoscopy dataset

Published:2021-05-27 Issue:1 Volume:8 Page:
ISSN:2052-4463
Container-title:Scientific Data
language:en
Short-container-title:Sci Data

Author:

Smedsrud Pia H.^ORCID,Thambawita Vajira^ORCID,Hicks Steven A.,Gjestang Henrik^ORCID,Nedrejord Oda Olsen^ORCID,Næss Espen,Borgli Hanna^ORCID,Jha Debesh^ORCID,Berstad Tor Jan Derek,Eskeland Sigrun L.,Lux Mathias,Espeland Håvard^ORCID,Petlund Andreas^ORCID,Nguyen Duc Tien Dang,Garcia-Ceja Enrique^ORCID,Johansen Dag,Schmidt Peter T.,Toth Ervin,Hammer Hugo L.,de Lange Thomas^ORCID,Riegler Michael A.^ORCID,Halvorsen Pål^ORCID

Abstract

AbstractArtificial 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.

Funder

Norges Forskningsråd

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability

Link

http://www.nature.com/articles/s41597-021-00920-z.pdf

Reference60 articles.

1. Greenwood-Van Meerveld, B., Johnson, A. C. & Grundy, D. Gastrointestinal physiology and function. In Gastrointestinal Pharmacology, 1–16 (Springer, 2017).

2. McLaughlin, P. D. & Maher, M. M. Primary malignant diseases of the small intestine. American Journal of Roentgenology 201, W9–W14 (2013).

3. Thomson, A. et al. Small bowel review: diseases of the small intestine. Digestive diseases and sciences 46, 2555–2566 (2001).

4. Enns, R. A. et al. Clinical practice guidelines for the use of video capsule endoscopy. Gastroenterology 152, 497–514 (2017).

5. Costamagna, G. et al. A prospective trial comparing small bowel radiographs and video capsule endoscopy for suspected small bowel disease. Gastroenterology 123, 999–1005 (2002).

Cited by 110 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Dynamic Attribute-guided Few-shot Open-set Network for medical image diagnosis;Expert Systems with Applications;2024-10

2. On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset;Applied Sciences;2024-09-10

3. Few-shot learning with task adaptation for multi-category gastrointestinal endoscopy classification;Biomedical Signal Processing and Control;2024-09

4. Application of Image Recognition Technology Using Deep Learning to the Medical and Agricultural Fields;IEEJ Transactions on Electronics, Information and Systems;2024-09-01

5. Pixel-wise annotation for clear and contaminated regions segmentation in wireless capsule endoscopy images: A multicentre database;Data in Brief;2024-09