Domain generalization for Diabetic Retinopathy (DR) classification allows a model to adeptly classify retinal images from previously unseen domains with various imaging conditions and patient demographics, thereby enhancing its applicability in a wide range of clinical environments. In this study, we explore the inherent capacity of variational autoencoders to disentangle the latent space of fundus images, with an aim to obtain a more robust and adaptable domain-invariant representation that effectively tackles the domain shift encountered in DR datasets. Despite the simplicity of our approach, we explore the efficacy of this classical method and demonstrate its ability to outperform contemporary state-of-the-art approaches for this task using publicly available datasets. Our findings challenge the prevailing assumption that highly sophisticated methods for DR classification are inherently superior for domain generalization. This highlights the importance of considering simple methods and adapting them to the challenging task of generalizing medical images, rather than solely relying on advanced techniques.

翻译：糖尿病视网膜病变（DR）分类的域泛化技术允许模型对来自未知域（具有不同成像条件和患者人口统计学特征）的视网膜图像进行精准分类，从而提升其在多种临床环境中的适用性。本研究通过探索变分自编码器对眼底图像潜在空间进行解耦的固有能力，旨在获得更鲁棒且可适应的域不变表示，以有效应对DR数据集中存在的域偏移问题。尽管方法设计简洁，我们验证了这一经典方法的效力，并证明其在使用公开数据集时能够超越当前最先进的域泛化方法。我们的研究结果挑战了"高阶DR分类方法在域泛化中必然更优"的主流假设，强调在医学图像泛化这一挑战性任务中，应当重视并适配简单方法，而非仅依赖复杂技术。