  This work reports the empirical performance of an automated medical landmark
detection method for predict clinical markers in hip radiograph images.
Notably, the detection method was trained using a label-only augmentation
scheme; our results indicate that this form of augmentation outperforms
traditional data augmentation and produces highly sample efficient estimators.
We train a generic U-Net-based architecture under a curriculum consisting of
two phases: initially relaxing the landmarking task by enlarging the label
points to regions, then gradually eroding these label regions back to the base
task. We measure the benefits of this approach on six datasets of radiographs
with gold-standard expert annotations.
