Single domain generalization aims to address the challenge of out-of-distribution generalization problem with only one source domain available. Feature distanglement is a classic solution to this purpose, where the extracted task-related feature is presumed to be resilient to domain shift. However, the absence of references from other domains in a single-domain scenario poses significant uncertainty in feature disentanglement (ill-posedness). In this paper, we propose a new framework, named \textit{Domain Game}, to perform better feature distangling for medical image segmentation, based on the observation that diagnostic relevant features are more sensitive to geometric transformations, whilist domain-specific features probably will remain invariant to such operations. In domain game, a set of randomly transformed images derived from a singular source image is strategically encoded into two separate feature sets to represent diagnostic features and domain-specific features, respectively, and we apply forces to pull or repel them in the feature space, accordingly. Results from cross-site test domain evaluation showcase approximately an ~11.8% performance boost in prostate segmentation and around ~10.5% in brain tumor segmentation compared to the second-best method.

翻译：单域泛化旨在解决仅有一个源域可用时的分布外泛化问题。特征解耦是解决该问题的经典方法，其假设提取的任务相关特征能够抵御域偏移的影响。然而，在单域场景下，由于缺乏其他域的参考，特征解耦存在显著的不确定性（病态性）。本文提出一种名为\textit{领域博弈}的新框架，以在医学图像分割中实现更优的特征解耦。该框架基于以下观察：诊断相关特征对几何变换更为敏感，而域特定特征对此类操作可能保持不变。在领域博弈中，从单一源图像生成的一组随机变换图像被策略性地编码为两个独立的特征集，分别表示诊断特征和域特定特征；随后我们在特征空间中相应地施加吸引力或排斥力以调控它们的关系。跨站点测试域的评估结果显示，在前列腺分割任务中性能较次优方法提升约11.8%，在脑肿瘤分割任务中提升约10.5%。