Fairness in artificial intelligence models has gained significantly more attention in recent years, especially in the area of medicine, as fairness in medical models is critical to people's well-being and lives. High-quality medical fairness datasets are needed to promote fairness learning research. Existing medical fairness datasets are all for classification tasks, and no fairness datasets are available for medical segmentation, while medical segmentation is an equally important clinical task as classifications, which can provide detailed spatial information on organ abnormalities ready to be assessed by clinicians. In this paper, we propose the first fairness dataset for medical segmentation named FairSeg with 10,000 subject samples. In addition, we propose a fair error-bound scaling approach to reweight the loss function with the upper error-bound in each identity group. We anticipate that the segmentation performance equity can be improved by explicitly tackling the hard cases with high training errors in each identity group. To facilitate fair comparisons, we propose new equity-scaled segmentation performance metrics, such as the equity-scaled Dice coefficient, which is calculated as the overall Dice coefficient divided by one plus the standard deviation of group Dice coefficients. Through comprehensive experiments, we demonstrate that our fair error-bound scaling approach either has superior or comparable fairness performance to the state-of-the-art fairness learning models. The dataset and code are publicly accessible via \url{https://github.com/Harvard-Ophthalmology-AI-Lab/FairSeg}.

本研究提出了第一个用于医学分割的公平数据集FairSeg，并提出公平误差界限缩放方法，通过显式处理每个身份群体中训练错误较高的难题，来改善分割性能公平性。通过全面的实验证明，该方法在公平性表现上优于或与现有的公平学习模型具有可比性。

FairSeg：針對公平性學習的大規模醫學圖像分割數據集，使用公平的錯誤邊界縮放