  Purpose Supervised deep learning in radiology suffers from notorious inherent
limitations: 1) It requires large, hand-annotated data sets, 2) It is
non-generalizable, and 3) It lacks explainability and intuition. We have
recently proposed Reinforcement Learning to address all threes. However, we
applied it to images with radiologist eye tracking points, which limits the
state-action space. Here we generalize the Deep-Q Learning to a gridworld-based
environment, so that only the images and image masks are required.
  Materials and Methods We trained a Deep Q network on 30 two-dimensional image
slices from the BraTS brain tumor database. Each image contained one lesion. We
then tested the trained Deep Q network on a separate set of 30 testing set
images. For comparison, we also trained and tested a keypoint detection
supervised deep learning network for the same set of training / testing images.
  Results Whereas the supervised approach quickly overfit the training data,
and predicably performed poorly on the testing set (11\% accuracy), the Deep-Q
learning approach showed progressive improved generalizability to the testing
set over training time, reaching 70\% accuracy.
  Conclusion We have shown a proof-of-principle application of reinforcement
learning to radiological images, here using 2D contrast-enhanced MRI brain
images with the goal of localizing brain tumors. This represents a
generalization of recent work to a gridworld setting, naturally suitable for
analyzing medical images.
