| 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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2024. 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 |
Computer Science, Artificial Intelligence |
| Manuscript Type |
Basic Study |
| Article Title |
Assessing deep learning models for multi-class upper endoscopic disease segmentation: A comprehensive comparative study
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
In Neng Chan, Pak Kin Wong, Tao Yan, Yan-Yan Hu, Chon In Chan, Ye-Ying Qin, Chi Hong Wong, In Weng Chan, Ieng Hou Lam, Sio Hou Wong, Zheng Li, Shan Gao, Hon Ho Yu, Liang Yao, Bao-Liang Zhao and Ying Hu |
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Guangdong Basic and Applied Basic Research Foundation |
No. 2021B1515130003 |
| Key Research and Development Plan of Hubei Province |
No. 2022BCE034 |
| Natural Science Foundation of Hubei Province |
No. 2024AFB1054 |
|
| Corresponding Author |
Pak Kin Wong, PhD, Professor, Department of Electromechanical Engineering, University of Macau, Avenida da Universidade, Taipa, Macau 999078, China. fstpkw@um.edu.mo |
| Key Words |
Deep learning; Upper endoscopy; Medical imaging; Gastrointestinal diseases; Disease segmentation |
| Core Tip |
This study evaluates 17 advanced deep learning models, including convolutional neural network-, transformer-, and mamba-based architectures, for multi-class upper gastrointestinal disease segmentation. Swin-UMamba achieves the highest segmentation accuracy, while SegFormer balances efficiency and performance. Automated segmentation demonstrates significant clinical value by improving diagnostic precision, reducing missed diagnoses, streamlining treatment planning, and easing physician workload. Key challenges include lighting variability, vague lesion boundaries, multi-label complexities, and dataset limitations. Future directions, such as multi-modal learning, self-supervised techniques, spatio-temporal modeling, and rigorous clinical validation, are essential to enhance model robustness and ensure applicability in diverse healthcare settings for better patient outcomes. |
| Publish Date |
2025-11-06 08:56 |
| Citation |
<p>Chan IN, Wong PK, Yan T, Hu YY, Chan CI, Qin YY, Wong CH, Chan IW, Lam IH, Wong SH, Li Z, Gao S, Yu HH, Yao L, Zhao BL, Hu Y. Assessing deep learning models for multi-class upper endoscopic disease segmentation: A comprehensive comparative study. <i>World J Gastroenterol</i> 2025; 31(41): 111184</p> |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i41/111184.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i41.111184 |
