Achieving flexible fairness metrics in federated medical imaging

The rapid adoption of Artificial Intelligence (AI) in medical imaging raises fairness and privacy concerns across demographic groups, especially in diagnosis and treatment decisions. While federated learning (FL) offers decentralized privacy preservation, current frameworks often prioritize collaboration fairness over group fairness, risking healthcare disparities. Here we present FlexFair, an innovative FL framework designed to address both fairness and privacy challenges. FlexFair incorporates a flexible regularization term to facilitate the integration of multiple fairness criteria, including equal accuracy, demographic parity, and equal opportunity. Evaluated across four clinical applications (polyp segmentation, fundus vascular segmentation, cervical cancer segmentation, and skin disease diagnosis), FlexFair outperforms state-of-the-art methods in both fairness and accuracy. Moreover, we curate a multi-center dataset for cervical cancer segmentation that includes 678 patients from four hospitals. This diverse dataset allows for a more comprehensive analysis of model performance across different population groups, ensuring the findings are applicable to a broader range of patients.

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Publication Details

Journal
Nature Communications
Published
2025-04-08
DOI
https://doi.org/10.1038/s41467-025-58549-0
Citations
10
Primary Topic
Radiation Dose and Imaging
Type
article
Field-Weighted Citation Impact
13.60
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article

Achieving flexible fairness metrics in federated medical imaging

Shuguang Cui, Tan Yu, Jinke Ren, Zihan Guo et al.
10 citations
Nature Communications
Radiation Dose and Imaging
13.60
article

Achieving flexible fairness metrics in federated medical imaging

Shuguang Cui, Tan Yu, Jinke Ren, Zihan Guo, Chun-Mei Feng, Xiaohui Duan, Huaqiao Xing, Xiaohua Ban, Yu Wang, Wei Wei, Zhen Li, Chuanlong Xie, Xian Liu, Yeyin Hu, Xuan Ding, Jun Wei, Rui Sun
article en
10 citations

Abstract

The rapid adoption of Artificial Intelligence (AI) in medical imaging raises fairness and privacy concerns across demographic groups, especially in diagnosis and treatment decisions. While federated learning (FL) offers decentralized privacy preservation, current frameworks often prioritize collaboration fairness over group fairness, risking healthcare disparities. Here we present FlexFair, an innovative FL framework designed to address both fairness and privacy challenges. FlexFair incorporates a flexible regularization term to facilitate the integration of multiple fairness criteria, including equal accuracy, demographic parity, and equal opportunity. Evaluated across four clinical applications (polyp segmentation, fundus vascular segmentation, cervical cancer segmentation, and skin disease diagnosis), FlexFair outperforms state-of-the-art methods in both fairness and accuracy. Moreover, we curate a multi-center dataset for cervical cancer segmentation that includes 678 patients from four hospitals. This diverse dataset allows for a more comprehensive analysis of model performance across different population groups, ensuring the findings are applicable to a broader range of patients.

Nature CommunicationsVol. 16(1)
Agency for Science, Technology and Research (SG), Guangzhou University of Chinese Medicine (CN), Sun Yat-sen University (CN), South China Normal University (CN), Beijing Normal University (CN), Institute of High Performance Computing (SG), Sun Yat-sen Memorial Hospital (CN), Chinese University of Hong Kong, Shenzhen (CN), Guangdong Province Women and Children Hospital (CN), Sun Yat-sen University Cancer Center (CN), State Key Laboratory of Oncology in South China
Openalex Percentile: Top 1%
Radiation Dose and Imaging
13.60
DOIOpen Access PDFOpenAlex
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