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.

通过探索变分自动编码器在眼底图像的潜在空间中解耦的内在能力，本研究旨在获取一种更强大适应不同 DR 数据集中遇到的领域转换的域不变表示方法，以提高该模型在广泛临床环境中的适用性，并证明其能够通过简单方法优于现有先进技术，挑战了对于 DR 分类高度复杂方法更优的普遍假设，强调了考虑简单方法并将其适应于医学图像的泛化任务的重要性。