  Deep learning models with large learning capacities often overfit to medical
imaging datasets. This is because training sets are often relatively small due
to the significant time and financial costs incurred in medical data
acquisition and labelling. Data augmentation is therefore often used to expand
the availability of training data and to increase generalization. However,
augmentation strategies are often chosen on an ad-hoc basis without
justification. In this paper, we present an augmentation policy search method
with the goal of improving model classification performance. We include in the
augmentation policy search additional transformations that are often used in
medical image analysis and evaluate their performance. In addition, we extend
the augmentation policy search to include non-linear mixed-example data
augmentation strategies. Using these learned policies, we show that principled
data augmentation for medical image model training can lead to significant
improvements in ultrasound standard plane detection, with an an average
F1-score improvement of 7.0% overall over naive data augmentation strategies in
ultrasound fetal standard plane classification. We find that the learned
representations of ultrasound images are better clustered and defined with
optimized data augmentation.
