Fine-grained pathomorphology recognition of cervical lesions with a dropped multibranch Swin Transformer
YoGaNet improved the fine-grained recognition of cervical cells in massive TCT images as compared with the baseline model and models reported in previous studies and may thus aid in improving early cervical cancer diagnosis. The inference code is available online (https://github.com/coding-with-chen/YoGA-Net-DMBST).
Authors
- Wenxuan Jiang (ORCID: https://orcid.org/0000-0003-4852-6055)
- Ming Xiong
- Yu Cheng (ORCID: https://orcid.org/0000-0003-4805-203X)
- Li Liu (ORCID: https://orcid.org/0000-0003-2566-7357)
- Feng Wu (ORCID: https://orcid.org/0000-0002-6911-4896)
- Yuanhua Wang (ORCID: https://orcid.org/0000-0003-4692-325X)
- Hao Wu (ORCID: https://orcid.org/0000-0002-5166-8882)
Institutions
- Army Medical University (CN)
- Daping Hospital (CN)
- Southwest Hospital (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-04-01
- DOI
- https://doi.org/10.21037/qims-24-1590
- Citations
- 1
- Primary Topic
- Cervical Cancer and HPV Research
- Type
- article
- Field-Weighted Citation Impact
- 1.35