Unsupervised Domain Adaptation for Anatomical Landmark Detection
Haibo Jin Haoxuan Che Hao Chen
Recently, anatomical landmark detection has achieved great progresses on
single-domain data, which usually assumes training and test sets are from the
same domain. However, such an assumption is not always true in practice, which
can cause significant performance drop due to domain shift. To tackle this
problem, we propose a novel framework for anatomical landmark detection under
the setting of unsupervised domain adaptation (UDA), which aims to transfer the
knowledge from labeled source domain to unlabeled target domain. The framework
leverages self-training and domain adversarial learning to address the domain
gap during adaptation. Specifically, a self-training strategy is proposed to
select reliable landmark-level pseudo-labels of target domain data with dynamic
thresholds, which makes the adaptation more effective. Furthermore, a domain
adversarial learning module is designed to handle the unaligned data
distributions of two domains by learning domain-invariant features via
adversarial training. Our experiments on cephalometric and lung landmark
detection show the effectiveness of the method, which reduces the domain gap by
a large margin and outperforms other UDA methods consistently. The code is
available at https://github.com/jhb86253817/UDA_Med_Landmark.