<div class="sc-jegwdG lhLRCf"><div class="sc-UEtKG dGqiYy sc-flttKd cguEtd"><div class="sc-fqwslf gsqkEc"><div class="sc-cBQMlg kAHhUk"><h2 class="sc-dcKlJK sc-cVttbi gqEuPW ksnHgj">About Dataset</h2></div></div></div><div class="sc-davvxH eCVTlP"><div class="sc-jCNfQM dTyvWO"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-gVIFzB gQKGyV"><h3>The Kvasir Dataset</h3>

<p><img src="https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41597-020-00622-y/MediaObjects/41597_2020_622_Fig1_HTML.png" alt=""></p>

<p>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. Here, 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 (ex- perienced endoscopists). In this respect, Kvasir is important for research on both single- and multi-disease computer aided detec- tion. By providing it, we invite and enable multimedia researcher into the medical domain of detection and retrieval.</p>

<h3>Background</h3>

<p>The human digestive system may be affected by several diseases. Altogether esophageal, stomach and colorectal cancer accounts for about 2.8 million new cases and 1.8 million deaths per year. Endoscopic examinations are the gold standards for investigation of the GI tract. Gastroscopy is an examination of the upper GI tract including esophagus, stomach and first part of small bowel, while colonoscopy covers the large bowel (colon) and rectum. Both these examinations are real-time video examinations of the inside of the GI tract by use of digital high definition endoscopes. Endoscopic examinations are resource demanding and requires both expensive technical equipment and trained personnel. For colorectal cancer prevention, endoscopic detection and removal of possible precancerous lesions are essential. Adenoma detection is therefore considered to be an important quality indicator in colorectal cancer screening. However, the ability to detect adenomas varies between doctors, and this may eventually affect the individuals’ risk of getting colorectal cancer. Endoscopic assessment of severity and sub-classification of different findings may also vary from one doctor to another. Accurate grading of diseases are important since it may influence decision-making on treatment and follow-up. For example, the degree of inflammation directly affects the choice of therapy in inflammatory bowel diseases (IBD). An objective and automated scoring system would therefore be highly welcomed. Automatic detection, recognition and assessment of pathological findings will probably contribute to reduce inequalities, improve quality and optimize use of scarce medical resources. Furthermore, since endoscopic examinations are real-time investigations, both normal and abnormal findings have to be recorded and documented within written reports. Automatic report generation would proba- bly contribute to reduce doctors’ time required for paperwork and thereby increase time to patient care. Reliable and careful docu- mentation with the use of minimal standard terminology (MST) may also contribute to improved patient follow-up and treatment. To our knowledge, a standardized and automatic reporting system that ensure high quality endoscopy reports does not exist. In order to make the health care system more scalable and cost effective, basic research in the intersection between computer science and medicine must go beyond traditional medical imaging by combining this area with multimedia data analysis and retrieval, artificial intelligence, and distributed processing. Next-generation medical big-data applications are a frontier for innovation, compe- tition and productivity, where there are currently large initiatives both in the EU and the US. In the area of multimedia research, people are starting to see the synergies between multimedia and medical systems. For automatic algorithmic detection of abnormalities in the GI tract, there have been many proposals from various research communities, especially for the topic of polyp detection. Hovever, the results are hard to reproduce due to lack of available medical data, i.e., the work listed above all use different and non-public data sets. Here, we therefore publish Kvasir: A Multi-Class Image Dataset for Computer Aided Gastrointestinal Disease Detection from the Vestre Viken Health Trust (Norway) containing not only polyps, but also two other findings, two classes related to polyp removal and three anatomical landmarks in the GI tract.</p>

<h3>Data Collection</h3>

<p>The data is collected using endoscopic equipment at Vestre Viken Health Trust (VV) in Norway. The VV consists of 4 hospitals and provides health care to 470.000 people. One of these hospitals (the Bærum Hospital) has a large gastroenterology department from where training data have been collected and will be provided, making the dataset larger in the future. Furthermore, the images are carefully annotated by one or more medical experts from VV and the Cancer Registry of Norway (CRN). The CRN provides new knowledge about cancer through research on cancer. It is part of South-Eastern Norway Regional Health Authority and is organized as an independent institution under Oslo University Hospital Trust. CRN is responsible for the national cancer screening programmes with the goal to prevent cancer death by discovering cancers or pre-cancerous lesions as early as possible.</p>

<h3>Dataset Details</h3>

<p>The Kvasir dataset consists of images, annotated and verified by medical doctors (experienced endoscopists), including several classes showing anatomical landmarks, phatological findings or endoscopic procedures in the GI tract, i.e., hundreds of images for each class. The number of images is sufficient to be used for different tasks, e.g., image retrieval, machine learning, deep learning and transfer learning, etc. The anatomical landmarks include Z-line, pylorus, cecum, etc., while the pathological finding includes esophagitis, polyps, ulcerative colitis, etc. In addition, we provide several set of images related to removal of lesions, e.g., "dyed and lifted polyp", the "dyed resection margins", etc. The dataset consist of the images with different resolution from 720x576 up to 1920x1072 pixels and organized in a way where they are sorted in separate folders named accordingly to the content. Some of the included classes of images have a green picture in picture illustrating the position and configuration of the endoscope inside the bowel, by use of an electromagnetic imaging system (ScopeGuide, Olympus Europe) that may support the interpretation of the image. This type of information may be important for later investigations (thus included), but must be handled with care for the detection of the endoscopic findings.</p>

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<p><a rel="noreferrer nofollow" aria-label="https://datasets.simula.no/kvasir/ (opens in a new tab)" target="_blank" href="https://datasets.simula.no/kvasir/">https://datasets.simula.no/kvasir/</a></p></div></div></div>