KVASIR

TLDR

Automatic disease detection by computers is an important but still unexplored field, and the scarcity of medical image datasets hampers reproducibility and comparison of approaches. This paper introduces KVASIR, a dataset of images from inside the gastrointestinal tract. The dataset contains images classified into three anatomical landmarks, three clinically significant findings, and two polyp‑removal categories, with sorting and annotation performed by experienced endoscopists. KVASIR supports research on single‑ and multi‑disease computer‑aided detection and invites multimedia researchers into medical detection and retrieval.

Abstract

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.