It has recently been shown that deep learning models for anatomical segmentation in medical images can exhibit biases against certain sub-populations defined in terms of protected attributes like sex or ethnicity. In this context, auditing fairness of deep segmentation models becomes crucial. However, such audit process generally requires access to ground-truth segmentation masks for the target population, which may not always be available, especially when going from development to deployment. Here we propose a new method to anticipate model biases in biomedical image segmentation in the absence of ground-truth annotations. Our unsupervised bias discovery method leverages the reverse classification accuracy framework to estimate segmentation quality. Through numerical experiments in synthetic and realistic scenarios we show how our method is able to successfully anticipate fairness issues in the absence of ground-truth labels, constituting a novel and valuable tool in this field.

翻译：近期研究表明，用于医学图像解剖结构分割的深度学习模型可能对受保护属性（如性别或种族）定义的特定子群表现出偏差。在此背景下，深度分割模型的公平性审计变得至关重要。然而，此类审计过程通常需要获取目标人群的真实分割掩膜，这在从开发到部署的过程中往往难以实现。本文提出一种新方法，可在缺乏真实标注的情况下预测生物医学图像分割中的模型偏差。我们的无监督偏差发现方法利用反向分类精度框架来估计分割质量。通过合成场景和真实场景的数值实验，我们展示了该方法如何在缺乏真实标签的情况下成功预测公平性问题，为该领域提供了新颖且有价值的工具。