Deep learning models often encounter challenges in making accurate inferences when there are domain shifts between the source and target data. This issue is particularly pronounced in clinical settings due to the scarcity of annotated data resulting from the professional and private nature of medical data. Although various cross-domain strategies have been explored, including frequency-based approaches that vary appearance while preserving semantics, many remain limited by data constraints and computational cost. To tackle domain shifts in data-scarce medical scenarios, we propose a Random frequency filtering enabled Single-source Domain Generalization algorithm (RaffeSDG), which promises robust out-of-domain inference with segmentation models trained on a single-source domain. A frequency filter-based data augmentation strategy is first proposed to promote domain variability within a single-source domain by introducing variations in frequency space and blending homologous samples. Then Gaussian filter-based structural saliency is also leveraged to learn robust representations across augmented samples, further facilitating the training of generalizable segmentation models. To validate the effectiveness of RaffeSDG, we conducted extensive experiments involving out-of-domain inference on segmentation tasks for three human tissues imaged by four diverse modalities. Through thorough investigations and comparisons, compelling evidence was observed in these experiments, demonstrating the potential and generalizability of RaffeSDG. The code is available at https://github.com/liamheng/Non-IID_Medical_Image_Segmentation.

翻译：深度学习模型在源数据与目标数据之间存在域偏移时，常面临准确推断的挑战。由于医学数据的专业性与私密性导致标注数据稀缺，该问题在临床环境中尤为突出。尽管已有多种跨域策略被探索，包括在保持语义的同时改变外观的基于频率的方法，但许多方法仍受限于数据约束和计算成本。为应对数据稀缺医学场景中的域偏移，我们提出了一种基于随机频率滤波的单源域泛化算法（RaffeSDG），该算法能够在仅使用单源域训练的模型上实现鲁棒的域外推断。首先提出一种基于频率滤波的数据增强策略，通过在频率空间引入变化并混合同源样本，促进单源域内的域变异性。同时利用基于高斯滤波的结构显著性来学习增强样本间的鲁棒表示，进一步促进可泛化分割模型的训练。为验证RaffeSDG的有效性，我们进行了大量实验，涉及对四种不同模态成像的三种人体组织分割任务进行域外推断。通过深入研究和比较，这些实验获得了令人信服的证据，证明了RaffeSDG的潜力与泛化能力。代码发布于 https://github.com/liamheng/Non-IID_Medical_Image_Segmentation。