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10/29/2021 12:16:53 PM | Browse: 378 | Download: 571
Publication Name World Journal of Clinical Cases
Manuscript ID 68624
Country China
Received
2021-06-15 11:51
Peer-Review Started
2021-06-15 11:51
To Make the First Decision
Return for Revision
2021-07-15 09:28
Revised
2021-07-26 03:19
Second Decision
2021-08-13 02:54
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief
2021-08-13 05:24
Articles in Press
2021-08-13 05:24
Publication Fee Transferred
Edit the Manuscript by Language Editor
2021-08-23 03:44
Typeset the Manuscript
2021-10-25 00:22
Publish the Manuscript Online
2021-10-29 12:16
ISSN 2307-8960 (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
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Publisher Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website http://www.wjgnet.com
Category Oncology
Manuscript Type Minireviews
Article Title Deep learning driven colorectal lesion detection in gastrointestinal endoscopic and pathological imaging
Manuscript Source Unsolicited Manuscript
All Author List Yu-Wen Cai, Fang-Fen Dong, Yu-Heng Shi, Li-Yuan Lu, Chen Chen, Ping Lin, Yu-Shan Xue, Jian-Hua Chen, Su-Yu Chen and Xiong-Biao Luo
ORCID
Author(s) ORCID Number
Yu-Wen Cai http://orcid.org/0000-0002-3022-8693
Fang-Fen Dong http://orcid.org/0000-0003-1535-6555
Yu-Heng Shi http://orcid.org/0000-0001-9483-4767
Li-Yuan Lu http://orcid.org/0000-0001-8612-2620
Chen Chen http://orcid.org/0000-0001-9809-4794
Ping Lin http://orcid.org/0000-0002-8547-8198
Yu-Shan Xue http://orcid.org/0000-0002-6464-1996
Jian-Hua Chen http://orcid.org/0000-0003-0433-9346
Su-Yu Chen http://orcid.org/0000-0002-0199-6925
Xiong-Biao Luo http://orcid.org/0000-0001-7906-8857
Funding Agency and Grant Number
Corresponding Author Su-Yu Chen, PhD, Academic Fellow, Chief Doctor, Department of Gastrointestinal Endoscopy, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, No. 420 Fuma Road, Jin'an District, Fuzhou 350014, Fujian Province, China. endosuyuchen@163.com
Key Words Deep learning; Artificial intelligence; Image analysis; Endoscopic; Colorectal lesions; Colorectal cancer
Core Tip The development of computer has promoted the progress of medical treatment. Artificial intelligence (AI) has been gradually applied in the medical field and achieved good results. The detection of colorectal lesions in the conventional gastrointestinal endoscopy is difficult, the diagnosis time is long, and there is often the problem of missed diagnosis and misdiagnosis. AI computer is a good aid for doctors. Through this review, we summarize the application of AI in the detection of colorectal lesions in recent years, in order to provide reference for the follow-up development and research.
Publish Date 2021-10-29 12:16
Citation Cai YW, Dong FF, Shi YH, Lu LY, Chen C, Lin P, Xue YS, Chen JH, Chen SY, Luo XB. Deep learning driven colorectal lesion detection in gastrointestinal endoscopic and pathological imaging. World J Clin Cases 2021; 9(31): 9376-9385
URL https://www.wjgnet.com/2307-8960/full/v9/i31/9376.htm
DOI https://dx.doi.org/10.12998/wjcc.v9.i31.9376
Full Article (PDF) WJCC-9-9376.pdf
Full Article (Word) WJCC-9-9376.docx
Manuscript File 68624_Auto_Edited-JPY_WangTQ.docx
Answering Reviewers 68624-Answering reviewers.pdf
Audio Core Tip 68624-Audio core tip.m4a
Conflict-of-Interest Disclosure Form 68624-Conflict-of-interest statement.pdf
Copyright License Agreement 68624-Copyright license agreement.pdf
Non-Native Speakers of English Editing Certificate 68624-Language certificate.pdf
Peer-review Report 68624-Peer-review(s).pdf
Scientific Misconduct Check 68624-Scientific misconduct check.pdf
Scientific Editor Work List 68624-Scientific editor work list.pdf