KVASIR: A Multi-Class Image Dataset for Computer Aided Gastrointestinal Disease Detection
By Konstantin Pogorelov, K. Randel, C. Griwodz, S. L. Eskeland, Thomas de Lange, Dag Johansen, Concetto Spampinato, Duc-Tien Dang-Nguyen, M. Lux, P. Schmidt and 2 more. ACM SIGMM Conference on Multimedia Systems, 2017
875 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition, Gastroenterology, Oncology
View Paper: https://www.semanticscholar.org/paper/1fea6fecdfe7b9429c9f61cba23fcd15a333867a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Andrew Zisserman
- Luc Van Gool
- Lawrence Carin
- Dacheng Tao
- Qiang Yang
- Jitendra Malik
- Yi Yang
- Trevor Darrell
In Oncology
- Debesh Jha
- Pål Halvorsen
- Michael Alexander Riegler
- Dag Johansen
- Haavard D. Johansen
- Thomas de Lange
- Pia Helen Smedsrud
- Radu Marculescu
Citations in the corpus
- Surgical data science – from concepts toward clinical translation (2020, background): “Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection, in: Proceedings of the 8th ACM on Multimedia Systems Conference, pp. 164–169.”
- Deep Learning for Medical Anomaly Detection – A Survey (2020, methodology): “2 (a) illustrates two examples from the Kvasir endoscopy image dataset [5].”
- ResUNet++: An Advanced Architecture for Medical Image Segmentation (2019, methodology): “2) We annotated the polyp class from the Kvasir dataset [7] with the help of an expert gastroenterologist to create the new Kvasir-SEG dataset [8].”
- Distribution-Free, Risk-Controlling Prediction Sets (2021, methodology): “We evaluated our method with an experiment combining several open-source polyp segmentation datasets: Kvasir [50], Hyper-Kvasir [51], CVC-ColonDB and CVC-ClinicDB [52], and ETIS-Larib [53].”
- A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation (2021, background): “For formal definitions and formulas, see the definitions in for example [4], [51].”
- Kvasir-SEG: A Segmented Polyp Dataset (2019, methodology): “The detailed explanation about these parameters can be found in the Kvasir dataset paper [16].”
- FLOP: Federated Learning on Medical Datasets using Partial Networks (2021, methodology): “The Kvasir dataset [24] concerns image classification for Gastrointestinal disease with eight classes.”