标题： 发散域，汇聚分级：提升糖尿病视网膜病变分级中的泛化能力 显示英文标题

标题： Divergent Domains, Convergent Grading: Enhancing Generalization in Diabetic Retinopathy Grading

摘要： 糖尿病视网膜病变（DR）占全球失明病例的5%。尽管许多深度学习方法试图改进传统的DR分级方法，但它们在面对新的分布外数据时往往表现不佳，从而阻碍了其广泛应用。在这项研究中，我们引入了一种新的深度学习方法，用于在DR分级中实现领域泛化（DG），并做出了以下贡献。 首先，我们提出了一种新的方式，根据原始眼底图像的等级生成与诊断相关的图像到图像的眼底增强。这些增强旨在模拟DR数据集中的类型变化，从而提高模型的鲁棒性。 其次，我们通过提出一种新的领域泛化特定损失——领域对齐损失，解决了标准分类损失在DR眼底数据集领域泛化中的局限性；该损失确保来自所有领域的特征向量对应于同一类别时收敛到相同的流形上，以实现更好的领域泛化。 第三，我们通过提出使用焦点损失来解决DR领域和类别间的数据不平衡问题，该损失无缝集成到我们的新对齐损失中。 第四，由于不可避免的DR诊断观察者差异导致标签噪声，我们建议利用自监督预训练。这种方法确保我们的DG模型即使在仅有限的非DR眼底图像数据集可用的情况下，也能对早期标签噪声保持稳健。 我们的方法在强大的经验风险最小化基线和最近提出的其他最先进的DR分级领域泛化方法上显示出显著的改进。 代码可在https://github.com/sharonchokuwa/dg-adr获取。 显示英文摘要

摘要： Diabetic Retinopathy (DR) constitutes 5% of global blindness cases. While numerous deep learning approaches have sought to enhance traditional DR grading methods, they often falter when confronted with new out-of-distribution data thereby impeding their widespread application. In this study, we introduce a novel deep learning method for achieving domain generalization (DG) in DR grading and make the following contributions. First, we propose a new way of generating image-to-image diagnostically relevant fundus augmentations conditioned on the grade of the original fundus image. These augmentations are tailored to emulate the types of shifts in DR datasets thus increase the model's robustness. Second, we address the limitations of the standard classification loss in DG for DR fundus datasets by proposing a new DG-specific loss, domain alignment loss; which ensures that the feature vectors from all domains corresponding to the same class converge onto the same manifold for better domain generalization. Third, we tackle the coupled problem of data imbalance across DR domains and classes by proposing to employ Focal loss which seamlessly integrates with our new alignment loss. Fourth, due to inevitable observer variability in DR diagnosis that induces label noise, we propose leveraging self-supervised pretraining. This approach ensures that our DG model remains robust against early susceptibility to label noise, even when only a limited dataset of non-DR fundus images is available for pretraining. Our method demonstrates significant improvements over the strong Empirical Risk Minimization baseline and other recently proposed state-of-the-art DG methods for DR grading. Code is available at https://github.com/sharonchokuwa/dg-adr.