The domain discrepancy existed between medical images acquired in different situations renders a major hurdle in deploying pre-trained medical image segmentation models for clinical use. Since it is less possible to distribute training data with the pre-trained model due to the huge data size and privacy concern, source-free unsupervised domain adaptation (SFDA) has recently been increasingly studied based on either pseudo labels or prior knowledge. However, the image features and probability maps used by pseudo label-based SFDA and the consistent prior assumption and the prior prediction network used by prior-guided SFDA may become less reliable when the domain discrepancy is large. In this paper, we propose a \textbf{Pro}mpt learning based \textbf{SFDA} (\textbf{ProSFDA}) method for medical image segmentation, which aims to improve the quality of domain adaption by minimizing explicitly the domain discrepancy. Specifically, in the prompt learning stage, we estimate source-domain images via adding a domain-aware prompt to target-domain images, then optimize the prompt via minimizing the statistic alignment loss, and thereby prompt the source model to generate reliable predictions on (altered) target-domain images. In the feature alignment stage, we also align the features of target-domain images and their styles-augmented counterparts to optimize the source model, and hence push the model to extract compact features. We evaluate our ProSFDA on two multi-domain medical image segmentation benchmarks. Our results indicate that the proposed ProSFDA outperforms substantially other SFDA methods and is even comparable to UDA methods. Code will be available at \url{https://github.com/ShishuaiHu/ProSFDA}.
翻译:在不同情况下获得的医疗图象之间存在差异,使得在为临床使用部署预先培训的医疗图象分割模型方面存在重大障碍。由于由于数据大小巨大和隐私问题,因此不太可能以预培训模型分发培训数据,最近越来越多地根据假标签或先前的知识研究无源、无监管域域貌调整(SFDA)方法,目的是通过明确缩小域差异来提高域调整质量。但具体地说,在迅速学习阶段,我们通过在目标-轨道图像上添加一个网域识别信号,然后通过尽可能减少统计校正损失来优化速度。因此,我们提议采用基于\ textbf{SFDA} (\ textbf{ProSFDA}) (\ textbf{ProSFDA}) 的学习模型,用于医学图象学分解的无源的无源域调和概率图象(我们SFDA) 的配置源代码模型将显示我们的目标-SFDA的校正图像的可靠性。