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KVASIR: A Multi-Class Image Dataset for Computer Aided Gastrointestinal Disease Detection

Authors:

Konstantin Pogorelov,

Kristin Ranheim Randel,

Carsten Griwodz,

Sigrun Losada Eskeland,

Thomas de Lange,

Concetto Spampinato,

Duc-Tien Dang-Nguyen,

Peter Thelin Schmidt,

Michael Riegler,

Pål HalvorsenAuthors Info & Claims

MMSys'17: Proceedings of the 8th ACM on Multimedia Systems Conference

Pages 164 - 169

https://doi.org/10.1145/3083187.3083212

Published: 20 June 2017 Publication History

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.

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cover image ACM Conferences

MMSys'17: Proceedings of the 8th ACM on Multimedia Systems Conference

June 2017

407 pages

ISBN:9781450350020

DOI:10.1145/3083187

Copyright © 2017 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 20 June 2017

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June 20 - 23, 2017

Taipei, Taiwan

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MMSys'17 Paper Acceptance Rate 13 of 47 submissions, 28%;

Overall Acceptance Rate 176 of 530 submissions, 33%

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https://doi.org/10.7717/peerj-cs.2059
Hossain TShamrat FZhou XMahmud IMazumder MSharmin SGururajan R(2024)Development of a multi-fusion convolutional neural network (MF-CNN) for enhanced gastrointestinal disease diagnosis in endoscopy image analysisPeerJ Computer Science10.7717/peerj-cs.195010(e1950)Online publication date: 19-Apr-2024
https://doi.org/10.7717/peerj-cs.1950
Al-Otaibi SRehman AMujahid MAlotaibi SSaba T(2024)Efficient-gastro: optimized EfficientNet model for the detection of gastrointestinal disorders using transfer learning and wireless capsule endoscopy imagesPeerJ Computer Science10.7717/peerj-cs.190210(e1902)Online publication date: 11-Mar-2024
https://doi.org/10.7717/peerj-cs.1902
Moghtaderi SYaghoobian OWahid KLukong K(2024)Endoscopic Image Enhancement: Wavelet Transform and Guided Filter Decomposition-Based Fusion ApproachJournal of Imaging10.3390/jimaging1001002810:1(28)Online publication date: 20-Jan-2024
https://doi.org/10.3390/jimaging10010028
Kim HCho BPark JKang B(2024)Color-Transfer-Enhanced Data Construction and Validation for Deep Learning-Based Upper Gastrointestinal Landmark Classification in Wireless Capsule EndoscopyDiagnostics10.3390/diagnostics1406059114:6(591)Online publication date: 11-Mar-2024
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