Accurate and robust medical image segmentation is fundamental and crucial for enhancing the autonomy of computer-aided diagnosis and intervention systems. Medical data collection normally involves different scanners, protocols, and populations, making domain adaptation (DA) a highly demanding research field to alleviate model degradation in the deployment site. To preserve the model performance across multiple testing domains, this work proposes the Curriculum-based Augmented Fourier Domain Adaptation (Curri-AFDA) for robust medical image segmentation. In particular, our curriculum learning strategy is based on the causal relationship of a model under different levels of data shift in the deployment phase, where the higher the shift is, the harder to recognize the variance. Considering this, we progressively introduce more amplitude information from the target domain to the source domain in the frequency space during the curriculum-style training to smoothly schedule the semantic knowledge transfer in an easier-to-harder manner. Besides, we incorporate the training-time chained augmentation mixing to help expand the data distributions while preserving the domain-invariant semantics, which is beneficial for the acquired model to be more robust and generalize better to unseen domains. Extensive experiments on two segmentation tasks of Retina and Nuclei collected from multiple sites and scanners suggest that our proposed method yields superior adaptation and generalization performance. Meanwhile, our approach proves to be more robust under various corruption types and increasing severity levels. In addition, we show our method is also beneficial in the domain-adaptive classification task with skin lesion datasets. The code is available at https://github.com/lofrienger/Curri-AFDA.

翻译：准确且鲁棒的医学图像分割对于提升计算机辅助诊断与干预系统的自主性至关重要。医学数据采集通常涉及不同扫描设备、协议和人群，这使得域自适应（DA）成为缓解模型在部署场景中性能退化的重要研究领域。为保持模型在多个测试域中的性能，本文提出基于课程增强的傅里叶域自适应方法（Curri-AFDA）用于鲁棒医学图像分割。具体而言，我们的课程学习策略基于模型在部署阶段面临不同层级数据偏移时的因果关系——偏移程度越高，识别差异的难度越大。基于此，我们在课程式训练中逐步将目标域的更多幅值信息引入源域的频率空间，以"由易到难"的方式平滑调度语义知识迁移。此外，我们引入训练时链式增强混合策略扩展数据分布，同时保留域不变语义特征，有助于训练模型更具鲁棒性并在未见域中实现更优泛化。在多个中心与扫描设备采集的视网膜和细胞核分割任务上的大量实验表明，所提方法实现了优越的自适应与泛化性能。同时，本方法在多种退化类型及递增严重程度下展现出更强的鲁棒性。此外，我们验证了该方法在皮肤病变数据集上的域自适应分类任务中同样有效。代码公开于 https://github.com/lofrienger/Curri-AFDA。