A novel image segmentation network with multi‐scale and flow‐guided attention for early screening of vaginal intraepithelial neoplasia (VAIN)
The proposed network exhibits exceptional performance and robustness in the task of VAIN image segmentation, effectively segmenting VAIN lesions and providing strong technical support for early VAIN screening and clinical diagnosis. This work has significant clinical application value.
Authors
- Yeye Song
- Haima Yang
- Jun Li (ORCID: https://orcid.org/0009-0004-8938-9055)
- Jin Liu (ORCID: https://orcid.org/0000-0003-0359-0248)
- Dawei Zhang (ORCID: https://orcid.org/0000-0002-0841-7826)
- Zhijie Shi
- Yuling Li (ORCID: https://orcid.org/0009-0007-9674-9057)
- Jinyu Lü
- Lihua Qiu (ORCID: https://orcid.org/0000-0003-3734-1741)
- Wenlong Xia
- Le Fu
Institutions
- Shanghai University of Engineering Science (CN)
- University of Shanghai for Science and Technology (CN)
- Shanghai Jiao Tong University (CN)
- Renji Hospital (CN)
- Academy of Opto-Electronics (CN)
- Shanghai First Maternity and Infant Hospital (CN)
- Shanxi Academy of Medical Sciences (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1002/mp.18041
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Key Laboratory of Space Active Opto-electronics Technology