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Kvasir: a multi-class image-dataset for computer aided gastrointestinal disease detection

Pogorelov, Konstantin, Randel, Kristin, Griwodz, Carsten, de Lange, Thomas, Eskeland, Sigrun, Johansen, Dag, Spampinato, Concetto, Dang-Nguyen, Duc-Tien orcid logoORCID: 0000-0002-2761-2213, Lux, Mathias, Schmidt, Peter, Riegler, Michael and Halvorsen, Pål (2017) Kvasir: a multi-class image-dataset for computer aided gastrointestinal disease detection. In: ACM Multimedia Systems (MMSys ’17), 20-23 June 2017, Taipei, Taiwan. ISBN 123-4567-24-567

Automatic detection of diseases by use of computers is an important, but still unexplored field of research. Such innovations may improve medical practice and refine health care systems all over the world. However, datasets containing medical images are hardly available, making reproducibility and comparison of approaches almost im- possible. In this paper, we present Kvasir, a dataset containing images from inside the gastrointestinal (GI) tract. The collection of images are classified into three important anatomical landmarks and three clinically significant findings. In addition, it contains two categories of images related to endoscopic polyp removal. Sorting and annotation of the dataset is performed by medical doctors (ex- perienced endoscopists). In this respect, Kvasir is important for research on both single- and multi-disease computer aided detec- tion. By providing it, we invite and enable multimedia researcher into the medical domain of detection and retrieval.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Subjects:Computer Science > Multimedia systems
DCU Faculties and Centres:Research Institutes and Centres > INSIGHT Centre for Data Analytics
Published in: MMSys'17 Proceedings of the 8th ACM on Multimedia Systems Conference. Proceedings of ACM on Multimedia Systems Conference . ACM. ISBN 123-4567-24-567
Official URL:http://dx.doi.org/10.1145/3083187.3083212
Copyright Information:© 2017 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in MMSys'17 Proceedings of the 8th ACM on Multimedia Systems Conference http://doi.acm.org/10.1145/3083187.3083212
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:21821
Deposited On:05 Jul 2017 11:19 by Duc-Tien Dang-Nguyen . Last Modified 08 Nov 2021 15:04

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