Skip to Main content Skip to Navigation
New interface
Journal articles

Deep reinforcement learning in medical imaging: A literature review

Abstract : 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.
Complete list of metadata

https://hal.inria.fr/hal-03375000
Contributor : Project-Team Asclepios Connect in order to contact the contributor
Submitted on : Tuesday, October 12, 2021 - 2:23:32 PM
Last modification on : Friday, November 18, 2022 - 9:26:40 AM

Links full text

Identifiers

Citation

Kevin Zhou, Hoang Ngan Le, Khoa Luu, Hien van Nguyen, Nicholas Ayache. Deep reinforcement learning in medical imaging: A literature review. Medical Image Analysis, 2021, 73, pp.102193. ⟨10.1016/j.media.2021.102193⟩. ⟨hal-03375000⟩

Share

Metrics

Record views

32