Unsupervised domain adaptation (UDA) has increasingly gained interests for
its capacity to transfer the knowledge learned from a labeled source domain to
an unlabeled target domain. However, typical UDA methods require concurrent
access to both the source and target domain data, which largely limits its
application in medical scenarios where source data is often unavailable due to
privacy concern. To tackle the source data-absent problem, we present a novel
two-stage source-free domain adaptation (SFDA) framework for medical image
segmentation, where only a well-trained source segmentation model and unlabeled
target data are available during domain adaptation. Specifically, in the
prototype-anchored feature alignment stage, we first utilize the weights of the
pre-trained pixel-wise classifier as source prototypes, which preserve the
information of source features. Then, we introduce the bi-directional transport
to align the target features with class prototypes by minimizing its expected
cost. On top of that, a contrastive learning stage is further devised to
utilize those pixels with unreliable predictions for a more compact target
feature distribution. Extensive experiments on a cross-modality medical
segmentation task demonstrate the superiority of our method in large domain
discrepancy settings compared with the state-of-the-art SFDA approaches and
even some UDA methods. Code is available at
this https URL

在医学图像分割领域，我们提出了一种新颖的两阶段无源域适应框架（SFDA），通过仅使用训练有素的源分割模型和无标签目标数据，旨在解决没有源数据时的问题。该框架首先利用预训练像素分类器的权重作为源原型，通过最小化预期成本，通过双向传输将目标特征与类原型对齐，其次设计了对比学习阶段，利用具有不可靠预测的像素来得到更紧凑的目标特征分布。实验证明，我们的方法在大域差异设置下优于最先进的 SFDA 方法甚至一些 UDA 方法。