  Deep neural networks (DNNs) show promise in breast cancer screening, but
their robustness to input perturbations must be better understood before they
can be clinically implemented. There exists extensive literature on this
subject in the context of natural images that can potentially be built upon.
However, it cannot be assumed that conclusions about robustness will transfer
from natural images to mammogram images, due to significant differences between
the two image modalities. In order to determine whether conclusions will
transfer, we measure the sensitivity of a radiologist-level screening mammogram
image classifier to four commonly studied input perturbations that natural
image classifiers are sensitive to. We find that mammogram image classifiers
are also sensitive to these perturbations, which suggests that we can build on
the existing literature. We also perform a detailed analysis on the effects of
low-pass filtering, and find that it degrades the visibility of clinically
meaningful features called microcalcifications. Since low-pass filtering
removes semantically meaningful information that is predictive of breast
cancer, we argue that it is undesirable for mammogram image classifiers to be
invariant to it. This is in contrast to natural images, where we do not want
DNNs to be sensitive to low-pass filtering due to its tendency to remove
information that is human-incomprehensible.
