The domain shift between training and testing data presents a significant challenge for training generalizable deep learning models. As a consequence, the performance of models trained with the independent and identically distributed (i.i.d) assumption deteriorates when deployed in the real world. This problem is exacerbated in the medical imaging context due to variations in data acquisition across clinical centers, medical apparatus, and patients. Domain generalization (DG) aims to address this problem by learning a model that generalizes well to any unseen target domain. Many domain generalization techniques were unsuccessful in learning domain-invariant representations due to the large domain shift. Furthermore, multiple tasks in medical imaging are not yet extensively studied in existing literature when it comes to DG point of view. In this paper, we introduce a DG method that re-establishes the model objective function as a maximization of mutual information with a large pretrained model to the medical imaging field. We re-visit the problem of DG in Diabetic Retinopathy (DR) classification to establish a clear benchmark with a correct model selection strategy and to achieve robust domain-invariant representation for an improved generalization. Moreover, we conduct extensive experiments on public datasets to show that our proposed method consistently outperforms the previous state-of-the-art by a margin of 5.25% in average accuracy and a lower standard deviation. Source code available at https://github.com/BioMedIA-MBZUAI/DGM-DR

翻译：训练数据与测试数据之间的域偏移是训练泛化能力强的深度学习模型所面临的重要挑战。基于独立同分布假设训练的模型在现实部署中性能会因此下降。在医学影像领域，由于临床中心、医疗设备及患者数据的采集差异，这一问题尤为突出。域泛化旨在通过学习一个能够良好泛化至任意未见目标域的模型来解决此问题。许多域泛化技术因域偏移过大而未能成功学习域不变表征。此外，从域泛化视角来看，现有文献对医学影像中多项任务的系统性研究尚不充分。本文提出一种域泛化方法，通过将模型目标函数重构为与大型预训练模型的互信息最大化，将其引入医学影像领域。我们重新审视糖尿病视网膜病变分类中的域泛化问题，通过正确的模型选择策略建立清晰基准，并获取稳健的域不变表征以提升泛化效果。此外，我们在公开数据集上开展大量实验，结果表明所提方法在平均准确率上持续超越此前最优方法5.25%，且标准差更低。源代码已发布至 https://github.com/BioMedIA-MBZUAI/DGM-DR