  The use of semantic segmentation for masking and cropping input images has
proven to be a significant aid in medical imaging classification tasks by
decreasing the noise and variance of the training dataset. However,
implementing this approach with classical methods is challenging: the cost of
obtaining a dense segmentation is high, and the precise input area that is most
crucial to the classification task is difficult to determine a-priori. We
propose a novel joint-training deep reinforcement learning framework for image
augmentation. A segmentation network, weakly supervised with policy gradient
optimization, acts as an agent, and outputs masks as actions given samples as
states, with the goal of maximizing reward signals from the classification
network. In this way, the segmentation network learns to mask unimportant
imaging features. Our method, Adversarial Policy Gradient Augmentation (APGA),
shows promising results on Stanford's MURA dataset and on a hip fracture
classification task with an increase in global accuracy of up to 7.33% and
improved performance over baseline methods in 9/10 tasks evaluated. We discuss
the broad applicability of our joint training strategy to a variety of medical
imaging tasks.
