mlsanityData to 7 Oct 2026

Reinforcement Learning in Medical Image Analysis: Concepts, Applications, Challenges, and Future Directions

arXiv 2206.14302 ↗First posted 28 Jun 2022cs.CV cs.LGcited by 15

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Abstract (arXiv)

Motivation: Medical image analysis involves tasks to assist physicians in qualitative and quantitative analysis of lesions or anatomical structures, significantly improving the accuracy and reliability of diagnosis and prognosis. Traditionally, these tasks are finished by physicians or medical physicists and lead to two major problems: (i) low efficiency; (ii) biased by personal experience. In the past decade, many machine learning methods have been applied to accelerate and automate the image analysis process. Compared to the enormous deployments of supervised and unsupervised learning models, attempts to use reinforcement learning in medical image analysis are scarce. This review article could serve as the stepping-stone for related research. Significance: From our observation, though reinforcement learning has gradually gained momentum in recent years, many researchers in the medical analysis field find it hard to understand and deploy in clinics. One cause is lacking well-organized review articles targeting readers lacking professional computer science backgrounds. Rather than providing a comprehensive list of all reinforcement learning models in medical image analysis, this paper may help the readers to learn how to formulate and solve their medical image analysis research as reinforcement learning problems. Approach & Results: We selected published articles from Google Scholar and PubMed. Considering the scarcity of related articles, we also included some outstanding newest preprints. The papers are carefully reviewed and categorized according to the type of image analysis task. We first review the basic concepts and popular models of reinforcement learning. Then we explore the applications of reinforcement learning models in landmark detection. Finally, we conclude the article by discussing the reviewed reinforcement learning approaches' limitations and possible improvements.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 39 of its 90 references that are in the map. First posted on arXiv 28 Jun 2022.

Builds on

39 of 90 references.

References · 39

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  4. 2015Continuous control with deep reinforcement learningcited by 3,279
  5. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  6. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  7. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  8. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  9. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  10. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  11. 2016Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivationcited by 282
  12. 2017Deep Reinforcement Learning framework for Autonomous Drivingcited by 224
27 more
  1. 2017DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentationcited by 110
  2. 2016Deep Successor Reinforcement Learningcited by 72
  3. 2019Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learningcited by 41
  4. 2016An Artificial Agent for Robust Image Registrationcited by 23
  5. 2019Multiple Landmark Detection using Multi-Agent Reinforcement Learningcited by 23
  6. 2017Dilated FCN for Multi-Agent 2D/3D Medical Image Registrationcited by 22
  7. 2017Learning to detect chest radiographs containing lung nodules using visual attention networkscited by 16
  8. 2020Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentationcited by 16
  9. 2018Iterative Interaction Training for Segmentation Editing Networkscited by 14
  10. 2018Learning Representations in Model-Free Hierarchical Reinforcement Learningcited by 13
  11. 2019Resource Optimized Neural Architecture Search for 3D Medical Image Segmentationcited by 13
  12. 2020Synthetic Sample Selection via Reinforcement Learningcited by 13
  13. 2021Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumescited by 12
  14. 2020Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNetcited by 11
  15. 2020Auto-weighting for Breast Cancer Classification in Multimodal Ultrasoundcited by 9
  16. 2020Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learningcited by 8
  17. 2020Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentationcited by 7
  18. 2020Deep Reinforcement Learning for Organ Localization in CTcited by 7
  19. 2020Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical imagescited by 7
  20. 2019Improving Skin Condition Classification with a Visual Symptom Checker Trained using Reinforcement Learningcited by 5
  21. 2021Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registrationcited by 5
  22. 2020Bayesian Conditional GAN for MRI Brain Image Synthesiscited by 4
  23. 2021Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 imagescited by 4
  24. 2021MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learningcited by 4
  25. 2020A reinforcement learning application of guided Monte Carlo Tree Search algorithm for beam orientation selection in radiation therapycited by 2
  26. 2019Automatic vocal tract landmark localization from midsagittal MRI datacited by 1
  27. 2021Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learningcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 5; 2025: 5; 2026: 1.

15 papers.

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  1. 2024Comparison of fine-tuning strategies for transfer learning in medical image classificationcited by 14
  2. 2024Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classificationcited by 12
  3. 2023Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Imagescited by 5
  4. 2024A Behavior-Aware Approach for Deep Reinforcement Learning in Non-stationary Environments without Known Change Pointscited by 5
  5. 2024Learning Low-Rank Feature for Thorax Disease Classificationcited by 4
  6. 2025Enhancing PPO with Trajectory-Aware Hybrid Policiescited by 4
  7. 2025Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasoundcited by 3
  8. 2022Deep learning and machine learning for Malaria detection: overview, challenges and future directionscited by 2
  9. 2024Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learningcited by 2
  10. 2025Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imagingcited by 2
  11. 2025Reinforcement Learning for Unsupervised Domain Adaptation in Spatio-Temporal Echocardiography Segmentationcited by 1
  12. 2023Deep Learning-based Multi-Organ CT Segmentation with Adversarial Data Augmentationcited by 0
3 more
  1. 2023Applications of machine Learning to improve the efficiency and range of microbial biosynthesis: a review of state-of-art techniquescited by 0
  2. 2025PPORLD-EDNetLDCT: A Proximal Policy Optimization-Based Reinforcement Learning Framework for Adaptive Low-Dose CT Denoisingcited by 0
  3. 2026AdaFuse: Adaptive Multimodal Fusion for Lung Cancer Risk Prediction via Reinforcement Learningcited by 0