Single‐View Contrastive Learning for Laryngeal Leukoplakia Classification With <scp>NBI</scp> Laryngoscopy Images
Experimental results demonstrate that our model achieves an accuracy of 96.12%, which is higher than the current mainstream models. Our model also achieves high specificity and sensitivity. The code is available at https://github.com/hans-bbt/single-view-contrastive-learning.
Authors
- Xiaoyong Ren (ORCID: https://orcid.org/0000-0002-0237-6103)
- Shuangli Du (ORCID: https://orcid.org/0000-0002-8897-0778)
- Zhenzhen You (ORCID: https://orcid.org/0000-0003-3882-4737)
- Haiqin Liu (ORCID: https://orcid.org/0000-0003-1702-0895)
- Zhiyong Lv
- Xinhong Hei (ORCID: https://orcid.org/0000-0002-6394-0492)
- Minghua Zhao (ORCID: https://orcid.org/0000-0001-9732-4869)
- Zhenghao Shi (ORCID: https://orcid.org/0000-0001-6123-2276)
- Yan Yan (ORCID: https://orcid.org/0009-0005-5151-8154)
- Botao Han
Institutions
- Second Affiliated Hospital of Xi'an Jiaotong University (CN)
- Xi'an University of Technology (CN)
Publication Details
- Journal
- Head & Neck
- Published
- 2025-04-27
- DOI
- https://doi.org/10.1002/hed.28157
- Primary Topic
- Head and Neck Cancer Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Basic Research Program of Shaanxi Province