  Noisy data present in medical imaging datasets can often aid the development
of robust models that are equipped to handle real-world data. However, if the
bad data contains insufficient anatomical information, it can have a severe
negative effect on the model's performance. We propose a novel methodology
using a semi-supervised Siamese network to identify bad data. This method
requires only a small pool of 'reference' medical images to be reviewed by a
non-expert human to ensure the major anatomical structures are present in the
Field of View. The model trains on this reference set and identifies bad data
by using the Siamese network to compute the distance between the reference set
and all other medical images in the dataset. This methodology achieves an Area
Under the Curve (AUC) of 0.989 for identifying bad data. Code will be available
at https://git.io/JYFuV.
