FairVision：通过公平身份缩放实现眼病筛查的公平深度学习
FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling
AI总结：
该研究针对3D医学成像模型公平性研究不足的问题，首次综合评估多架构模型在眼病筛查中的多属性偏差，提出公平身份缩放方法提升性能与公平性，并发布含3万受试者的Harvard-FairVision大规模数据集。
摘要：
医疗领域人工智能的公平性至关重要，因为它直接影响人类福祉。尽管2D医学成像的公平性研究已取得进展，但3D模型的公平性仍未得到充分探索，主要受限于3D公平数据集规模较小。由于在当前最优（SOTA）临床诊疗中3D成像优于2D成像，因此研究这些3D模型的公平性十分关键。为填补这一研究空白，我们首次针对3D医学成像模型在多个受保护属性上的公平性开展了综合研究。我们的研究涵盖2D和3D模型，在三种常见眼病上评估了五种架构的公平性，发现模型在种族、性别和族裔方面存在显著偏差。为缓解这些偏差，我们提出了一种新颖的公平身份缩放（FIS）方法，该方法能同时提升整体性能和公平性，表现优于多种SOTA公平性方法。此外，我们发布了Harvard-FairVision数据集，这是首个包含30000名受试者的大规模医疗公平数据集，兼具2D和3D成像数据以及六个人口统计身份属性。Harvard-FairVision为影响全球约3.8亿人的三种主要眼部疾病提供了标签，可作为2D和3D公平学习的宝贵资源。我们的代码和数据集可在https://ophai.hms.harvard.edu/datasets/harvard-fairvision30k 公开获取。
英文摘要：
Equity in AI for healthcare is crucial due to its direct impact on human well-being. Despite advancements in 2D medical imaging fairness, the fairness of 3D models remains underexplored, hindered by the small sizes of 3D fairness datasets. Since 3D imaging surpasses 2D imaging in SOTA clinical care, it is critical to understand the fairness of these 3D models. To address this research gap, we conduct the first comprehensive study on the fairness of 3D medical imaging models across multiple protected attributes. Our investigation spans both 2D and 3D models and evaluates fairness across five architectures on three common eye diseases, revealing significant biases across race, gender, and ethnicity. To alleviate these biases, we propose a novel fair identity scaling (FIS) method that improves both overall performance and fairness, outperforming various SOTA fairness methods. Moreover, we release Harvard-FairVision, the first large-scale medical fairness dataset with 30,000 subjects featuring both 2D and 3D imaging data and six demographic identity attributes. Harvard-FairVision provides labels for three major eye disorders affecting about 380 million people worldwide, serving as a valuable resource for both 2D and 3D fairness learning. Our code and dataset are publicly accessible at \url{https://ophai.hms.harvard.edu/datasets/harvard-fairvision30k}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
