  Deep reinforcement learning (DRL) augments the reinforcement learning
framework, which learns a sequence of actions that maximizes the expected
reward, with the representative power of deep neural networks. Recent works
have demonstrated the great potential of DRL in medicine and healthcare. This
paper presents a literature review of DRL in medical imaging. We start with a
comprehensive tutorial of DRL, including the latest model-free and model-based
algorithms. We then cover existing DRL applications for medical imaging, which
are roughly divided into three main categories: (I) parametric medical image
analysis tasks including landmark detection, object/lesion detection,
registration, and view plane localization; (ii) solving optimization tasks
including hyperparameter tuning, selecting augmentation strategies, and neural
architecture search; and (iii) miscellaneous applications including surgical
gesture segmentation, personalized mobile health intervention, and
computational model personalization. The paper concludes with discussions of
future perspectives.
