  Machine learning applications in medical imaging are frequently limited by
the lack of quality labeled data. In this paper, we explore the self training
method, a form of semi-supervised learning, to address the labeling burden. By
integrating reinforcement learning, we were able to expand the application of
self training to complex segmentation networks without any further human
annotation. The proposed approach, reinforced self training (ReST), fine tunes
a semantic segmentation networks by introducing a policy network that learns to
generate pseudolabels. We incorporate an expert demonstration network, based on
inverse reinforcement learning, to enhance clinical validity and convergence of
the policy network. The model was tested on a pulmonary nodule segmentation
task in chest X-rays and achieved the performance of a standard U-Net while
using only 50% of the labeled data, by exploiting unlabeled data. When the same
number of labeled data was used, a moderate to significant cross validation
accuracy improvement was achieved depending on the absolute number of labels
used.
