Diabetic retinopathy (DR). is caused by long-standing diabetes and is among the fifth leading cause for visual impairments. The process of early diagnosis and treatments could be helpful in curing the disease, however, the detection procedure is rather challenging and mostly tedious. Therefore, automated diabetic retinopathy classification using deep learning techniques has gained interest in the medical imaging community. Akin to several other real-world applications of deep learning, the typical assumption of i.i.d data is also violated in DR classification that relies on deep learning. Therefore, developing DR classification methods robust to unseen distributions is of great value. In this paper, we study the problem of generalizing a model to unseen distributions or domains (a.k.a domain generalization) in DR classification. To this end, we propose a simple and effective domain generalization (DG) approach that achieves self-distillation in vision transformers (ViT) via a novel prediction softening mechanism. This prediction softening is an adaptive convex combination one-hot labels with the model's own knowledge. We perform extensive experiments on challenging open-source DR classification datasets under both multi-source and single-source DG settings with three different ViT backbones to establish the efficacy and applicability of our approach against competing methods. For the first time, we report the performance of several state-of-the-art DG methods on open-source DR classification datasets after conducting thorough experiments. Finally, our method is also capable of delivering improved calibration performance than other methods, showing its suitability for safety-critical applications, including healthcare. We hope that our contributions would investigate more DG research across the medical imaging community.

翻译：糖尿病视网膜病变（DR）由长期糖尿病引起，是导致视力障碍的第五大病因。早期诊断与治疗有助于缓解病情，然而其检测过程既充满挑战又极为繁琐。因此，利用深度学习技术实现糖尿病视网膜病变的自动化分类引起了医学影像领域的广泛兴趣。与深度学习的其他实际应用类似，依赖深度学习的DR分类同样违反了数据独立同分布（i.i.d.）的典型假设。因此，开发对未见过分布具有鲁棒性的DR分类方法具有重要价值。本文研究了DR分类中模型泛化至未见过分布或领域（即领域泛化）的问题。为此，我们提出了一种简单有效的领域泛化（DG）方法，通过新颖的预测软化机制在视觉Transformer（ViT）中实现自蒸馏。该预测软化是一种将独热标签与模型自身知识进行自适应凸组合的机制。我们在多源和单源两种DG设置下，使用三种不同的ViT骨干网络，对具有挑战性的开源DR分类数据集进行了广泛实验，以验证本方法相比竞争方法的有效性和适用性。首次通过全面实验，我们报告了多种最新DG方法在开源DR分类数据集上的性能表现。此外，本方法在校准性能上也优于其他方法，展现了其在医疗保健等安全关键应用中的适用性。我们希望本研究能推动医学影像领域对DG的更多探索。