Deep reinforcement learning in medical imaging: A literature review
- Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
- University of Arkansas（阿肯色大学）
- University of Houston（休斯顿大学）
- INRIA, Sophia Antipolis-Mediterranean Centre（法国国家信息与自动化研究所索菲亚·安蒂波利斯地中海中心）
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英文摘要：
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.
