  In the pharmaceutical industry the screening of opaque vaccines containing
suspensions is currently a manual task carried out by trained human visual
inspectors. We show that deep learning can be used to effectively automate this
process. A moving contrast is required to distinguish anomalies from other
particles, reflections and dust resting on a vial's surface. We train
3D-ConvNets to predict the likelihood of 20-frame video samples containing
anomalies. Our unaugmented dataset consists of hand-labelled samples, recorded
using vials provided by the HAL Allergy Group, a pharmaceutical company. We
trained ten randomly initialized 3D-ConvNets to provide a benchmark, observing
mean AUROC scores of 0.94 and 0.93 for positive samples (containing anomalies)
and negative (anomaly-free) samples, respectively. Using Frame-Completion
Generative Adversarial Networks we: (i) introduce an algorithm for computing
saliency maps, which we use to verify that the 3D-ConvNets are indeed
identifying anomalies; (ii) propose a novel self-training approach using the
saliency maps to determine if multiple networks agree on the location of
anomalies. Our self-training approach allows us to augment our data set by
labelling 217,888 additional samples. 3D-ConvNets trained with our augmented
dataset improve on the results we get when we train only on the unaugmented
dataset.
