  The success of deep learning methods relies on the availability of a large
number of datasets with annotations; however, curating such datasets is
burdensome, especially for medical images. To relieve such a burden for a
landmark detection task, we explore the feasibility of using only a single
annotated image and propose a novel framework named Cascade Comparing to Detect
(CC2D) for one-shot landmark detection. CC2D consists of two stages: 1)
Self-supervised learning (CC2D-SSL) and 2) Training with pseudo-labels
(CC2D-TPL). CC2D-SSL captures the consistent anatomical information in a
coarse-to-fine fashion by comparing the cascade feature representations and
generates predictions on the training set. CC2D-TPL further improves the
performance by training a new landmark detector with those predictions. The
effectiveness of CC2D is evaluated on a widely-used public dataset of
cephalometric landmark detection, which achieves a competitive detection
accuracy of 81.01\% within 4.0mm, comparable to the state-of-the-art
fully-supervised methods using a lot more than one training image.
