  As an important upstream task for many medical applications, supervised
landmark localization still requires non-negligible annotation costs to achieve
desirable performance. Besides, due to cumbersome collection procedures, the
limited size of medical landmark datasets impacts the effectiveness of
large-scale self-supervised pre-training methods. To address these challenges,
we propose a two-stage framework for one-shot medical landmark localization,
which first infers landmarks by unsupervised registration from the labeled
exemplar to unlabeled targets, and then utilizes these noisy pseudo labels to
train robust detectors. To handle the significant structure variations, we
learn an end-to-end cascade of global alignment and local deformations, under
the guidance of novel loss functions which incorporate edge information. In
stage II, we explore self-consistency for selecting reliable pseudo labels and
cross-consistency for semi-supervised learning. Our method achieves
state-of-the-art performances on public datasets of different body parts, which
demonstrates its general applicability.
