Most statistical learning algorithms rely on an over-simplified assumption, that is, the train and test data are independent and identically distributed. In real-world scenarios, however, it is common for models to encounter data from new and different domains to which they were not exposed to during training. This is often the case in medical imaging applications due to differences in acquisition devices, imaging protocols, and patient characteristics. To address this problem, domain generalization (DG) is a promising direction as it enables models to handle data from previously unseen domains by learning domain-invariant features robust to variations across different domains. To this end, we introduce a novel DG method called Adversarial Intensity Attack (AdverIN), which leverages adversarial training to generate training data with an infinite number of styles and increase data diversity while preserving essential content information. We conduct extensive evaluation experiments on various multi-domain segmentation datasets, including 2D retinal fundus optic disc/cup and 3D prostate MRI. Our results demonstrate that AdverIN significantly improves the generalization ability of the segmentation models, achieving significant improvement on these challenging datasets. Code is available upon publication.

翻译：大多数统计算法基于一个过度简化的假设，即训练数据和测试数据独立同分布。然而，在现实场景中，模型常会遇到训练时未接触过的新领域数据。由于采集设备、成像协议和患者特征的差异，这种情况在医学影像应用中尤为常见。为解决此问题，域泛化（Domain Generalization, DG）作为一种有前景的方向，能够通过学习对不同域变化鲁棒的域不变特征，使模型处理来自未见域的数据。为此，我们提出了一种名为抗性强度攻击（Adversarial Intensity Attack, AdverIN）的新型域泛化方法，该方法利用对抗训练生成具有无限风格的训练数据，在保留关键内容信息的同时增加数据多样性。我们在多种多域分割数据集上进行了广泛评估实验，包括2D视网膜眼底视盘/视杯和3D前列腺MRI。结果表明，AdverIN显著提升了分割模型的泛化能力，在这些具有挑战性的数据集上取得了显著改进。代码将在论文发表后提供。