Deep learning-based anatomical site classification for upper gastrointestinal endoscopy

He, Q, Bano, S, Ahmad, OF et al. (6 more authors) (2020) Deep learning-based anatomical site classification for upper gastrointestinal endoscopy. International Journal of Computer Assisted Radiology and Surgery. ISSN 1861-6410

Abstract

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Copyright, Publisher and Additional Information: © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: Artificial intelligence; Endoscopy; Gastroenterology; Deep learning
Dates:
  • Accepted: 31 March 2020
  • Published (online): 6 May 2020
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds)
Funding Information:
FunderGrant number
EPSRC (Engineering and Physical Sciences Research Council)EP/P027938/1
Royal Societywm150122
Depositing User: Symplectic Publications
Date Deposited: 02 Apr 2020 13:33
Last Modified: 03 Jun 2020 13:44
Status: Published online
Publisher: Springer Nature
Identification Number: https://doi.org/10.1007/s11548-020-02148-5

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