Frequency-mixed Single-source Domain Generalization for Medical Image
  Segmentation
Heng Li Haojin Li Wei Zhao Huazhu Fu Xiuyun Su Yan Hu Jiang Liu
The annotation scarcity of medical image segmentation poses challenges in
collecting sufficient training data for deep learning models. Specifically,
models trained on limited data may not generalize well to other unseen data
domains, resulting in a domain shift issue. Consequently, domain generalization
(DG) is developed to boost the performance of segmentation models on unseen
domains. However, the DG setup requires multiple source domains, which impedes
the efficient deployment of segmentation algorithms in clinical scenarios. To
address this challenge and improve the segmentation model's generalizability,
we propose a novel approach called the Frequency-mixed Single-source Domain
Generalization method (FreeSDG). By analyzing the frequency's effect on domain
discrepancy, FreeSDG leverages a mixed frequency spectrum to augment the
single-source domain. Additionally, self-supervision is constructed in the
domain augmentation to learn robust context-aware representations for the
segmentation task. Experimental results on five datasets of three modalities
demonstrate the effectiveness of the proposed algorithm. FreeSDG outperforms
state-of-the-art methods and significantly improves the segmentation model's
generalizability. Therefore, FreeSDG provides a promising solution for
enhancing the generalization of medical image segmentation models, especially
when annotated data is scarce. The code is available at
https://github.com/liamheng/Non-IID_Medical_Image_Segmentation.