Federated Learning for Predicting Postoperative Remission of Patients with Acromegaly: A Multicentered Study
We demonstrate that the DFL workflow without data sharing should be a more appropriate method in ML tasks in multicentered studies. And the DFL workflow should be further exploited in clinical researches in other departments and it can encourage and facilitate multicentered studies.
Authors
- Shuangjian Yang
- Ruijian Wu
- Xuexue Bai (ORCID: https://orcid.org/0000-0002-3841-6453)
- Xueyang Wu (ORCID: https://orcid.org/0000-0001-5419-7273)
- Renzhi Wang (ORCID: https://orcid.org/0000-0003-2080-5474)
- Ming Feng (ORCID: https://orcid.org/0000-0002-1666-2717)
- He Wang (ORCID: https://orcid.org/0000-0002-2053-9439)
- Qian Xu (ORCID: https://orcid.org/0000-0002-9180-3487)
- Xiaoxu Li (ORCID: https://orcid.org/0000-0003-2662-1385)
- Qiang Yang (ORCID: https://orcid.org/0000-0002-4445-929X)
- Wentai Zhang (ORCID: https://orcid.org/0000-0003-1876-6242)
- Xiao-Hai Liu (ORCID: https://orcid.org/0000-0002-6159-3140)
- Congcong Deng
Institutions
- University of Southern California (US)
- Capital Medical University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Hong Kong University of Science and Technology (HK)
- Peking University (CN)
- Peking Union Medical College Hospital (CN)
- Peking University First Hospital (CN)
Publication Details
- Journal
- World Neurosurgery
- Published
- 2024-11-19
- DOI
- https://doi.org/10.1016/j.wneu.2024.10.091
- Citations
- 7
- Primary Topic
- Pituitary Gland Disorders and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 2.50