  Though analysis of Medical Images by Deep Learning achieves unprecedented
results across various applications, the effect of \emph{noisy training
annotations} is rarely studied in a systematic manner. In Medical Image
Analysis, most reports addressing this question concentrate on studying
segmentation performance of deep learning classifiers. The absence of
continuous ground truth annotations in these studies limits the value of
conclusions for applications, where regression is the primary method of choice.
In the application of surgical instrument pose estimation, where precision has
a direct clinical impact on patient outcome, studying the effect of \emph{noisy
annotations} on deep learning pose estimation techniques is of supreme
importance. Real x-ray images are inadequate for this evaluation due to the
unavailability of ground truth annotations. We circumvent this problem by
generating synthetic radiographs, where the ground truth pose is known and
therefore the pose estimation error made by the medical-expert can be estimated
from experiments. Furthermore, this study shows the property of deep neural
networks to learn dominant signals from noisy annotations with sufficient data
in a regression setting.
