Detecting and localizing cervical lesions in colposcopic images with deep semantic feature mining
The proposed approach demonstrates promising capabilities in identifying normal and cervical lesions, particularly excelling in lesion area segmentation. Its accuracy in guiding biopsy site selection and subsequent localization treatment is satisfactory, offering valuable support to healthcare professionals in disease assessment and management.
Authors
- Jingjing Weng
- Ruiyun Chen (ORCID: https://orcid.org/0009-0009-6051-6803)
- Ying Shi (ORCID: https://orcid.org/0000-0003-2244-5791)
- Zehao Yu (ORCID: https://orcid.org/0000-0002-7290-8005)
- Li Wang (ORCID: https://orcid.org/0009-0007-5755-6105)
- Jinghui Zhang (ORCID: https://orcid.org/0000-0003-3350-9682)
- Chengbin Peng (ORCID: https://orcid.org/0000-0002-7445-2638)
- Siming Zheng (ORCID: https://orcid.org/0000-0002-2823-3901)
- Huiping Li
Institutions
- Ningbo University (CN)
- Ningbo Medical Center Lihuili Hospital (CN)
- Ningbo First Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2024-11-22
- DOI
- https://doi.org/10.3389/fonc.2024.1423782
- Citations
- 4
- Primary Topic
- Cervical Cancer and HPV Research
- Type
- article
- Field-Weighted Citation Impact
- 1.55
Funders
- Natural Science Foundation of Ningbo