The HyperKvasir Dataset

Artificial intelligence is currently a hot topic in medicine. The fact that medical data is often sparse for various reasons leads to technical limitations. In this respect, we share the HyperKvasir dataset, which is the largest multi-class image and video dataset from the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at a Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos where it captures anatomical landmarks and pathological and normal findings giving in total around 1 million images and video frames.

A zip of the complete dataset can be downloaded at https://datasets.simula.no/hyper-kvasir

The paper describing the dataset (which should be cited when the dataset is used), is found here: https://doi.org/10.1038/s41597-020-00622-y

Additional data created by the experiments and source code can be found in this Github repository: https://github.com/simula/hyper-kvasir

人工智能目前已成为医学领域的热门话题。由于种种原因，医学数据往往较为稀缺，这导致了技术上的限制。鉴于此，我们共享了HyperKvasir数据集，它是目前可用的最大规模的胃肠道多类图像和视频数据集。该数据集在挪威一家医院的实际胃镜和结肠镜检查过程中收集，部分由经验丰富的胃肠道内镜专家进行标注。数据集包含110,079张图像和374个视频，捕捉了解剖标志、病理和正常发现，总计约一百万张图像和视频帧。

HyperKvasir数据集的完整压缩包可从以下链接下载：https://datasets.simula.no/hyper-kvasir

描述该数据集的论文（在使用数据集时应当引用），可在此处找到：https://doi.org/10.1038/s41597-020-00622-y

实验产生的额外数据和源代码可在此GitHub仓库找到：https://github.com/simula/hyper-kvasir