Artificial intelligence for early gastric cancer boundary recognition in NBI and nF-NBI endoscopic images
The proposed deep learning models accurately delineated EGC boundaries in NBI and NF-NBI images, achieving diagnostic performance comparable to that of senior endoscopists.
Authors
- Bilin Wang (ORCID: https://orcid.org/0000-0002-6704-6199)
- Junbo Li (ORCID: https://orcid.org/0000-0001-8794-8491)
- Ying Mei (ORCID: https://orcid.org/0000-0002-8508-4076)
- Yifei Ouyang (ORCID: https://orcid.org/0000-0002-9092-8165)
- Yunbo Guo (ORCID: https://orcid.org/0000-0001-6804-4884)
- Changda Lei (ORCID: https://orcid.org/0000-0001-7908-7011)
- Yuguo Tang
- Rui Li (ORCID: https://orcid.org/0000-0002-7298-3347)
- Kaicheng Hong
- Xiuji Kan
- Deqing Zhang (ORCID: https://orcid.org/0000-0002-5709-6088)
Institutions
- University of Science and Technology of China (CN)
- Suzhou University of Science and Technology (CN)
- Soochow University (CN)
- Suzhou Institute of Biomedical Engineering and Technology (CN)
- First Affiliated Hospital of Soochow University (CN)
Publication Details
- Journal
- Scandinavian Journal of Gastroenterology
- Published
- 2025-06-02
- DOI
- https://doi.org/10.1080/00365521.2025.2509818
- Citations
- 2
- Primary Topic
- Gastric Cancer Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 3.04