FedCMC: a federated learning model with contribution fairness based on multi-center core data extraction for assessing the myometrial invasion status of endometrial cancer
FedCMC effectively alleviates fairness issues in traditional FL frameworks and accurately predicts the myometrial invasion (MI) status of EC patients, supporting personalized treatment strategies.
Authors
- Xiaohong Ruan (ORCID: https://orcid.org/0009-0009-6287-9376)
- Junqi Sun (ORCID: https://orcid.org/0000-0002-7284-9826)
- Xiaodong Wen (ORCID: https://orcid.org/0000-0001-5626-8581)
- Dong Xie (ORCID: https://orcid.org/0009-0001-9103-8017)
- Ximiao Wang
- Yujian Zou
- Wansheng Long (ORCID: https://orcid.org/0000-0002-1331-1858)
- Yuping Li
- Mingwei Li (ORCID: https://orcid.org/0000-0002-2552-3225)
- Yuan Chen (ORCID: https://orcid.org/0000-0003-0334-2594)
- Peijun Li (ORCID: https://orcid.org/0000-0003-0701-3122)
- Bao Feng (ORCID: https://orcid.org/0000-0001-6389-8055)
- Jiangfeng Shi
- Changye Zheng
Institutions
- Guilin University of Aerospace Technology (CN)
- Shantou University (CN)
- Guilin University of Technology (CN)
- Third Affiliated Hospital of Southern Medical University (CN)
- Yue Bei People's Hospital (CN)
- Dongguan People’s Hospital (CN)
- Kaiping Central Hospital (CN)
- Jiangmen Central Hospital (CN)
- Yueqing People's Hospital (CN)
- Guilin University of Electronic Technology (CN)
- Southern Medical University (CN)
- South China University of Technology (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-09-09
- DOI
- https://doi.org/10.3389/fonc.2025.1648502
- Citations
- 1
- Primary Topic
- Endometrial and Cervical Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 2.06
Funders
- National Natural Science Foundation of China