Fairness (also known as equity interchangeably) in machine learning is important for societal well-being, but limited public datasets hinder its progress. Currently, no dedicated public medical datasets with imaging data for fairness learning are available, though underrepresented groups suffer from more health issues. To address this gap, we introduce Harvard Glaucoma Fairness (Harvard-GF), a retinal nerve disease dataset including 3,300 subjects with both 2D and 3D imaging data and balanced racial groups for glaucoma detection. Glaucoma is the leading cause of irreversible blindness globally with Blacks having doubled glaucoma prevalence than other races. We also propose a fair identity normalization (FIN) approach to equalize the feature importance between different identity groups. Our FIN approach is compared with various state-of-the-art fairness learning methods with superior performance in the racial, gender, and ethnicity fairness tasks with 2D and 3D imaging data, demonstrating the utilities of our dataset Harvard-GF for fairness learning. To facilitate fairness comparisons between different models, we propose an equity-scaled performance measure, which can be flexibly used to compare all kinds of performance metrics in the context of fairness. The dataset and code are publicly accessible via https://ophai.hms.harvard.edu/datasets/harvard-gf3300/.

中文翻译：

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哈佛青光眼公平性：用于公平学习和公平身份标准化的视网膜神经疾病数据集


机器学习中的公平性（也称为公平性）对社会福祉很重要，但有限的公共数据集阻碍了其进步。目前，没有用于公平学习的带有成像数据的专用公共医学数据集，尽管代表性不足的群体遭受了更多的健康问题。为了解决这一差距，我们引入了哈佛青光眼公平性 （Harvard-GF），这是一个视网膜神经疾病数据集，包括 3,300 名受试者，具有 2D 和 3D 成像数据以及用于青光眼检测的平衡种族群体。青光眼是全球不可逆失明的主要原因，黑人的青光眼患病率是其他种族的两倍。我们还提出了一种公平身份规范化 （FIN） 方法，以平衡不同身份组之间的特征重要性。我们的 FIN 方法与各种最先进的公平学习方法进行了比较，这些方法在种族、性别和民族公平任务中具有 2D 和 3D 成像数据表现出色，展示了我们的数据集 Harvard-GF 在公平学习方面的效用。为了促进不同模型之间的公平性比较，我们提出了一种公平尺度的绩效衡量标准，它可以灵活地用于在公平性背景下比较各种绩效指标。数据集和代码可通过 https://ophai.hms.harvard.edu/datasets/harvard-gf3300/ 公开访问。