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4/28/2021 2:31:45 PM | Browse: 27 | Download: 18
Publication Name Artificial Intelligence in Gastrointestinal Endoscopy
Manuscript ID 64304
Country/Territory China
2021-02-15 03:22
Peer-Review Started
2021-02-15 03:24
To Make the First Decision
Return for Revision
2021-03-19 15:06
2021-03-30 05:13
Second Decision
2021-04-19 06:46
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief
2021-04-20 05:09
Articles in Press
2021-04-20 05:09
Publication Fee Transferred
Edit the Manuscript by Language Editor
2021-04-28 01:25
Typeset the Manuscript
2021-04-28 09:02
Publish the Manuscript Online
2021-04-28 12:41
ISSN 2689-7164 (online)
Open Access This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Copyright © The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Article Reprints For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
Permissions For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website http://www.wjgnet.com
Category Gastroenterology and Hepatology
Manuscript Type Minireviews
Article Title Application of deep learning in image recognition and diagnosis of gastric cancer
Manuscript Source Invited Manuscript
All Author List Yu Li, Da Zhou, Tao-Tao Liu and Xi-Zhong Shen
Funding Agency and Grant Number
Funding Agency Grant Number
National Natural Science Foundation of China 81800510
Shanghai Sailing Program 18YF1415900
Corresponding Author Da Zhou, PhD, Doctor, Research Fellow, Department of Gastroenterology and Hepatology, Zhongshan Hospital Affiliated to Fudan University, 180 Feng Lin road, Shanghai 200032, China. mubing2007@foxmail.com
Key Words Endoscope; Artificial intelligence; Algorithm optimization; Data support
Core Tip Gastric cancer is a life-threatening disease with a high mortality rate. With the development of deep learning in the image processing of gastrointestinal endoscope, the efficiency and accuracy of gastric cancer diagnosis through imaging technology have been greatly improved. At present, there is no comprehensive summary on the graphic recognition method for gastric cancer based on deep learning. In this review, some gastric cancer image databases and mainstream gastric cancer recognition models were summarized to make a prospect for the application of deep learning in this field.
Publish Date 2021-04-28 12:41
Citation Li Y, Zhou D, Liu TT, Shen XZ. Application of deep learning in image recognition and diagnosis of gastric cancer. Artif Intell Gastrointest Endosc 2021; 2(2): 12-24
URL https://www.wjgnet.com/2689-7164/full/v2/i2/12.htm
DOI https://dx.doi.org/10.37126/aige.v2.i2.12
Full Article (PDF) AIGE-2-12.pdf
Full Article (Word) AIGE-2-12.docx
Manuscript File 64304_Auto_Edited.docx
Answering Reviewers 64304-Answering reviewers.pdf
Audio Core Tip 64304-Audio core tip.m4a
Conflict-of-Interest Disclosure Form 64304-Conflict-of-interest statement.pdf
Copyright License Agreement 64304-Copyright license agreement.pdf
Non-Native Speakers of English Editing Certificate 64304-Language certificate.pdf
Peer-review Report 64304-Peer-review(s).pdf
Scientific Misconduct Check 64304-Scientific misconduct check.pdf
Scientific Editor Work List 64304-Scientific editor work list.pdf