Fairness is an important topic for medical image analysis, driven by the challenge of unbalanced training data among diverse target groups and the societal demand for equitable medical quality. In response to this issue, our research adopts a data-driven strategy-enhancing data balance by integrating synthetic images. However, in terms of generating synthetic images, previous works either lack paired labels or fail to precisely control the boundaries of synthetic images to be aligned with those labels. To address this, we formulate the problem in a joint optimization manner, in which three networks are optimized towards the goal of empirical risk minimization and fairness maximization. On the implementation side, our solution features an innovative Point-Image Diffusion architecture, which leverages 3D point clouds for improved control over mask boundaries through a point-mask-image synthesis pipeline. This method outperforms significantly existing techniques in synthesizing scanning laser ophthalmoscopy (SLO) fundus images. By combining synthetic data with real data during the training phase using a proposed Equal Scale approach, our model achieves superior fairness segmentation performance compared to the state-of-the-art fairness learning models. Code is available at https://github.com/wenyi-li/FairDiff.

医学图像分析中，公平性是一个重要的主题，为了解决训练数据在不同目标群体之间存在的不平衡性和社会对医疗质量公平的需求，本研究采用了一种数据驱动策略，通过集成合成图像来增强数据平衡。为了解决生成合成图像时存在的问题，我们将问题以联合优化的方式进行建模，并通过创新的Point-Image Diffusion架构使用三维点云来改善蒙版边界的控制，从而在合成扫描激光眼底镜（SLO）视网膜图像方面显著优于现有技术。通过在训练阶段使用提出的Equal Scale方法将合成数据与真实数据相结合，我们的模型在公平分割性能方面优于最先进的公平学习模型。

公平分割与点图传播