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Kvasir: A Multi-Class Image-Dataset for Computer Aided Gastrointestinal Disease Detection


Konstantin Pogorelov, Sigrun Losada Eskeland, Thomas de Lange, Carsten Griwodz, Kristin Ranheim Randel, Håkon Kvale Stensland, Duc Tien Dang Nguyen, Concetto Spampinato, Dag Johansen, Michael Riegler, Pål Halvorsen

Publication Type: 
Refereed Conference Meeting Proceeding
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 impossible. 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 (experienced endoscopists). In this respect, Kvasir is important for research on both single- and multi-disease computer aided detection. By providing it, we invite and enable multimedia researcher into the medical domain of detection and retrieval.
Conference Name: 
ACM Multimedia System 2017
ACM Multimedia System 2017
Digital Object Identifer (DOI): 
Publication Date: 
Conference Location: 
Taiwan, Province of China
Research Group: 
Dublin City University (DCU)
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