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Articles Published Processes
5/21/2025 5:53:54 AM | Browse: 12 | Download: 30
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Received |
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2025-01-05 13:05 |
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Peer-Review Started |
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2025-01-05 13:05 |
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To Make the First Decision |
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Return for Revision |
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2025-02-13 22:19 |
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Revised |
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2025-03-12 07:39 |
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Second Decision |
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2025-04-27 02:41 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-04-27 07:04 |
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Articles in Press |
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2025-04-27 07:04 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-05-14 06:08 |
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Publish the Manuscript Online |
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2025-05-21 05:53 |
| ISSN |
1007-9327 (print) and 2219-2840 (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) 2025. 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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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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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 |
Pathology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Application of deep learning models in the pathological classification and staging of esophageal cancer: A focus on Wave-Vision Transformer
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Wei Wei, Xiao-Lei Zhang, Hong-Zhen Wang, Lin-Lin Wang, Jing-Li Wen, Xin Han and Qian Liu |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Wei Wei, PhD, Department of Oncology, Dongying People’s Hospital, No. 317 Dongcheng South Road, Dongying District, Dongying Dongying, Shandong Province, China. ww19810122@163.com |
| Key Words |
Esophageal cancer; Deep learning; Wave-Vision Transformer; Pathological classification; Staging; Early detection |
| Core Tip |
This study demonstrates the application of deep learning models, particularly Wave-Vision Transformer, for the pathological classification and staging of esophageal cancer. Wave-Vision Transformer outperformed other models such as transformer, residual network, and multi-layer perceptron, achieving the highest accuracy of 88.97% with low computational complexity. This innovative approach shows promise for improving early detection and personalized treatment strategies for esophageal cancer, potentially enhancing clinical outcomes in real-time applications. |
| Publish Date |
2025-05-21 05:53 |
| Citation |
<p>Wei W, Zhang XL, Wang HZ, Wang LL, Wen JL, Han X, Liu Q. Application of deep learning models in the pathological classification and staging of esophageal cancer: A focus on Wave-Vision Transformer. <i>World J Gastroenterol</i> 2025; 31(19): 104897</p> |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i19/104897.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i19.104897 |
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