Kvasir (The Kvasir Dataset)

Introduced by Konstantin Pogorelov et al. in KVASIR: A Multi-Class Image Dataset for Computer Aided Gastrointestinal Disease Detection

The KVASIR Dataset was released as part of the medical multimedia challenge presented by MediaEval. It is based on images obtained from the GI tract via an endoscopy procedure. The dataset is composed of images that are annotated and verified by medical doctors, and captures 8 different classes. The classes are based on three anatomical landmarks (z-line, pylorus, cecum), three pathological findings (esophagitis, polyps, ulcerative colitis) and two other classes (dyed and lifted polyps, dyed resection margins) related to the polyp removal process. Overall, the dataset contains 8,000 endoscopic images, with 1,000 image examples per class.

Source: Two-Stream Deep Feature Modelling for Automated Video Endoscopy Data Analysis


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