Abstract
Training robust learning algorithms across different medical imaging modalities is challenging due to the large domain gap. Unsupervised domain adaptation (UDA) mitigates this problem by using annotated images from the source domain and unlabeled images from the target domain to train the deep models. Existing approaches often rely on GAN-based style transfer, but these methods struggle to capture cross-domain mappings in regions with high variability. In this paper, we propose a unified framework, Bézier Meets Diffusion, for cross-domain image generation. First, we introduce a Bézier-curve-based style transfer strategy that effectively reduces the domain gap between source and target domains. The transferred source images enable the training of a more robust segmentation model across domains. Thereafter, using pseudo-labels generated by this segmentation model on the target domain, we train a conditional diffusion model (CDM) to synthesize high-quality, labeled target-domain images. To mitigate the impact of noisy pseudo-labels, we further develop an uncertainty-guided score matching method that improves the robustness of CDM training. Extensive experiments on public datasets demonstrate that our approach generates realistic labeled images, significantly augmenting the target domain and improving segmentation performance.
Abstract (translated)
在不同医学影像模态上训练鲁棒的学习算法具有挑战性，因为存在较大的领域差距。无监督域适应（UDA）通过使用源领域的标注图像和目标领域的未标注图像来训练深度模型，从而缓解了这一问题。现有方法通常依赖于基于GAN的样式迁移技术，但这些方法难以捕捉高变异区域中的跨域映射关系。 在本文中，我们提出了一种统一框架，“贝塞尔遇见扩散”（Bézier Meets Diffusion），用于跨领域图像生成。首先，我们引入了一个基于贝塞尔曲线的样式迁移策略，该策略有效地减少了源领域和目标领域之间的差距。迁移后的源域图像使我们能够在多个领域中训练出更稳健的分割模型。随后，利用此分割模型在目标领域的伪标签，我们训练一个条件扩散模型（CDM）来合成高质量、带标签的目标领域图像。 为了减轻噪声伪标签的影响，我们进一步开发了一种基于不确定性的得分匹配方法以提高CDM训练过程中的鲁棒性。在公共数据集上的大量实验表明，我们的方法能够生成逼真的标注图像，显著扩充了目标域，并提高了分割性能。
URL
https://arxiv.org/abs/2509.22476
