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-license: cc-by-4.0
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-# Dataset Card for HyperKvasir
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-## Dataset Summary
-
-HyperKvasir is the largest publicly available gastrointestinal (GI) endoscopy dataset, comprising 110,079 images and 374 videos collected during real gastro- and colonoscopy examinations at Bærum Hospital in Norway. The dataset includes anatomical landmarks, pathological findings, and normal findings, making it suitable for various computer vision tasks in medical imaging.
-
-## Supported Tasks and Leaderboards
-
-- ^[Image Classification]({"attribution":{"attributableIndex":"675-1"}})
-- ^[Object Detection]({"attribution":{"attributableIndex":"675-2"}})
-- ^[Semantic Segmentation]({"attribution":{"attributableIndex":"675-3"}})
-- ^[Anomaly Detection]({"attribution":{"attributableIndex":"675-4"}})
-- ^[Video Summarization]({"attribution":{"attributableIndex":"675-5"}}) [oai_citation:1‡datasets.simula.no](https://datasets.simula.no/hyper-kvasir/?utm_source=chatgpt.com) [oai_citation:2‡Papers with Code](https://paperswithcode.com/dataset/hyper-kvasir-dataset?utm_source=chatgpt.com) [oai_citation:3‡Dataset Ninja](https://datasetninja.com/hyper-kvasir?utm_source=chatgpt.com)
-
-## Languages
-
-- ^[English (metadata and annotations)]({"attribution":{"attributableIndex":"832-1"}}) [oai_citation:4‡datasets.simula.no](https://datasets.simula.no/hyper-kvasir/?utm_source=chatgpt.com)
-
-## Dataset Structure
-
-### Data Splits
-
-^[The dataset is divided into the following subsets:]({"attribution":{"attributableIndex":"887-2"}})
-
-- **Labeled Images**: ^[10,662 images across 23 classes (e.g., polyps, esophagitis, ulcers).]({"attribution":{"attributableIndex":"981-1"}})
-- **Unlabeled Images**: ^[99,417 images without annotations.]({"attribution":{"attributableIndex":"981-3"}})
-- **Segmented Images**: ^[1,000 polyp images with corresponding segmentation masks and bounding boxes.]({"attribution":{"attributableIndex":"981-5"}})
-- **Annotated Videos**: ^[373 videos totaling approximately 11.62 hours and 1,059,519 frames, with 171 annotated findings.]({"attribution":{"attributableIndex":"981-7"}}) [oai_citation:5‡datasets.simula.no](https://datasets.simula.no/hyper-kvasir/?utm_source=chatgpt.com)
-
-### Data Format
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-- **Images**: ^[JPEG format.]({"attribution":{"attributableIndex":"1363-2"}})
-- **Videos**: ^[AVI format.]({"attribution":{"attributableIndex":"1363-4"}})
-- **Segmentations**: ^[JPEG masks and JSON files for bounding boxes.]({"attribution":{"attributableIndex":"1363-6"}})
-- **Annotations**: ^[Provided in accompanying metadata files.]({"attribution":{"attributableIndex":"1363-8"}}) [oai_citation:6‡Academia](https://www.academia.edu/66510029/Hyper_Kvasir_A_Comprehensive_Multi_Class_Image_and_Video_Dataset_for_Gastrointestinal_Endoscopy?utm_source=chatgpt.com) [oai_citation:7‡datasets.simula.no](https://datasets.simula.no/kvasir-vqa/?utm_source=chatgpt.com) [oai_citation:8‡datasets.simula.no](https://datasets.simula.no/downloads/hyper-kvasir/?utm_source=chatgpt.com)
-
-## Dataset Creation
-
-### Curation Rationale
-
-^[The dataset was created to facilitate research in computer-aided diagnosis and detection in GI endoscopy by providing a large, diverse, and annotated dataset.]({"attribution":{"attributableIndex":"1570-2"}}) [oai_citation:9‡Academia](https://www.academia.edu/66510029/Hyper_Kvasir_A_Comprehensive_Multi_Class_Image_and_Video_Dataset_for_Gastrointestinal_Endoscopy?utm_source=chatgpt.com)
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-### Source Data
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-- **Collection**: ^[Data were collected during routine examinations using standard endoscopic equipment.]({"attribution":{"attributableIndex":"1778-2"}})
-- **Annotations**: ^[Performed by experienced gastroenterologists.]({"attribution":{"attributableIndex":"1778-4"}}) [oai_citation:10‡PMC](https://pmc.ncbi.nlm.nih.gov/articles/PMC9954881/?utm_source=chatgpt.com)
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-### Annotations
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-- **Labeled Images**: ^[Categorized into 23 classes based on findings.]({"attribution":{"attributableIndex":"1969-2"}})
-- **Segmented Images**: ^[Polyp regions manually segmented.]({"attribution":{"attributableIndex":"1969-4"}})
-- **Videos**: ^[Annotated for specific findings and landmarks.]({"attribution":{"attributableIndex":"1969-6"}}) [oai_citation:11‡datasets.simula.no](https://datasets.simula.no/hyper-kvasir/?utm_source=chatgpt.com)
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-## Licensing Information
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-^[The dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.]({"attribution":{"attributableIndex":"2182-1"}})
-
-## Citation Information
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-^[If you use this dataset, please cite:]({"attribution":{"attributableIndex":"2314-1"}})
-
-^[Borgli, H., Thambawita, V., Smedsrud, P.H., Hicks, S., Jha, D., Eskeland, S.L., Randel, K.R., Pogorelov, K., Lux, M., Nguyen, D.T.D., Johansen, D., Griwodz, C., Stensland, H.K., Garcia-Ceja, E., Schmidt, P.T., Hammer, H.L., Riegler, M.A., Halvorsen, P., & de Lange, T. (2020). HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. *Scientific Data*, 7(1), 283. https://doi.org/10.1038/s41597-020-00622-y]({"attribution":{"attributableIndex":"2381-0"}}) [oai_citation:12‡simula.no](https://www.simula.no/research/hyperkvasir-comprehensive-multi-class-image-and-video-dataset-gastrointestinal-endoscopy?utm_source=chatgpt.com)
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-## Contributions
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-- **Data Collection**: ^[Bærum Hospital, Norway.]({"attribution":{"attributableIndex":"2832-2"}})
-- **Annotation**: ^[Experienced gastroenterologists.]({"attribution":{"attributableIndex":"2832-4"}})
-- **Dataset Preparation**: ^[Simula Research Laboratory.]({"attribution":{"attributableIndex":"2832-6"}}) [oai_citation:13‡ResearchGate](https://www.researchgate.net/publication/338090796_Hyper-Kvasir_A_Comprehensive_Multi-Class_Image_and_Video_Dataset_for_Gastrointestinal_Endoscopy?utm_source=chatgpt.com)
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-## Dataset Links
-
-- **Homepage**: ^[https://datasets.simula.no/hyper-kvasir/]({"attribution":{"attributableIndex":"3011-2"}})
-- **Download**: ^[https://datasets.simula.no/downloads/hyper-kvasir/]({"attribution":{"attributableIndex":"3011-4"}})
-- **GitHub Repository**: ^[https://github.com/simula/hyper-kvasir]({"attribution":{"attributableIndex":"3011-6"}}) [oai_citation:14‡datasets.simula.no](https://datasets.simula.no/downloads/keep/?utm_source=chatgpt.com) [oai_citation:15‡OSF](https://osf.io/mh9sj/?utm_source=chatgpt.com)