Deep reinforcement learning to detect brain lesions on MRI: a
  proof-of-concept application of reinforcement learning to medical images
  Purpose: AI in radiology is hindered chiefly by: 1) Requiring large annotated
data sets. 2) Non-generalizability that limits deployment to new scanners /
institutions. And 3) Inadequate explainability and interpretability. We believe
that reinforcement learning can address all three shortcomings, with robust and
intuitive algorithms trainable on small datasets. To the best of our knowledge,
reinforcement learning has not been directly applied to computer vision tasks
for radiological images. In this proof-of-principle work, we train a deep
reinforcement learning network to predict brain tumor location.
  Materials and Methods: Using the BraTS brain tumor imaging database, we
trained a deep Q network on 70 post-contrast T1-weighted 2D image slices. We
did so in concert with image exploration, with rewards and punishments designed
to localize lesions. To compare with supervised deep learning, we trained a
keypoint detection convolutional neural network on the same 70 images. We
applied both approaches to a separate 30 image testing set.
  Results: Reinforcement learning predictions consistently improved during
training, whereas those of supervised deep learning quickly diverged.
Reinforcement learning predicted testing set lesion locations with 85%
accuracy, compared to roughly 7% accuracy for the supervised deep network.
  Conclusion: Reinforcement learning predicted lesions with high accuracy,
which is unprecedented for such a small training set. We believe that
reinforcement learning can propel radiology AI well past the inherent
limitations of supervised deep learning, with more clinician-driven research
and finally toward true clinical applicability.
