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

arXiv: https://arxiv.org/abs/2206.14302 · HTML: https://mlsanity.com/paper/2206.14302/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 90 references that are in the map.
- First posted on arXiv 28 Jun 2022.

## Builds on (most cited first, 30 of 39)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation](https://mlsanity.com/paper/1604.06057/) (2016)
- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation](https://mlsanity.com/paper/1707.00652/) (2017)
- [Deep Successor Reinforcement Learning](https://mlsanity.com/paper/1606.02396/) (2016)
- [Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1911.10334/) (2019)
- [An Artificial Agent for Robust Image Registration](https://mlsanity.com/paper/1611.10336/) (2016)
- [Multiple Landmark Detection using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1907.00318/) (2019)
- [Dilated FCN for Multi-Agent 2D/3D Medical Image Registration](https://mlsanity.com/paper/1712.01651/) (2017)
- [Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation](https://mlsanity.com/paper/2006.05847/) (2020)
- [Learning to detect chest radiographs containing lung nodules using visual attention networks](https://mlsanity.com/paper/1712.00996/) (2017)
- [Iterative Interaction Training for Segmentation Editing Networks](https://mlsanity.com/paper/1807.08555/) (2018)
- [Learning Representations in Model-Free Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1810.10096/) (2018)
- [Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/1909.00548/) (2019)
- [Synthetic Sample Selection via Reinforcement Learning](https://mlsanity.com/paper/2008.11331/) (2020)
- [Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes](https://mlsanity.com/paper/2106.09812/) (2021)
- [Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet](https://mlsanity.com/paper/2006.14702/) (2020)
- [Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound](https://mlsanity.com/paper/2008.03435/) (2020)
- [Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning](https://mlsanity.com/paper/2002.03733/) (2020)
- [Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation](https://mlsanity.com/paper/2002.09703/) (2020)
- [Deep Reinforcement Learning for Organ Localization in CT](https://mlsanity.com/paper/2005.04974/) (2020)

## Built on by (most cited first, 16 of 16)

- [Comparison of fine-tuning strategies for transfer learning in medical image classification](https://mlsanity.com/paper/2406.10050/) (2024)
- [Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2403.07939/) (2024)
- [Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Images](https://mlsanity.com/paper/2302.09682/) (2023)
- [A Behavior-Aware Approach for Deep Reinforcement Learning in Non-stationary Environments without Known Change Points](https://mlsanity.com/paper/2405.14214/) (2024)
- [Learning Low-Rank Feature for Thorax Disease Classification](https://mlsanity.com/paper/2404.18933/) (2024)
- [Enhancing PPO with Trajectory-Aware Hybrid Policies](https://mlsanity.com/paper/2502.15968/) (2025)
- [Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound](https://mlsanity.com/paper/2503.20685/) (2025)
- [Deep learning and machine learning for Malaria detection: overview, challenges and future directions](https://mlsanity.com/paper/2209.13292/) (2022)
- [Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learning](https://mlsanity.com/paper/2406.17902/) (2024)
- [Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging](https://mlsanity.com/paper/2503.16543/) (2025)
- [Reinforcement Learning for Unsupervised Domain Adaptation in Spatio-Temporal Echocardiography Segmentation](https://mlsanity.com/paper/2510.14244/) (2025)
- [Deep Learning-based Multi-Organ CT Segmentation with Adversarial Data Augmentation](https://mlsanity.com/paper/2302.13172/) (2023)
- [Applications of machine Learning to improve the efficiency and range of microbial biosynthesis: a review of state-of-art techniques](https://mlsanity.com/paper/2308.13877/) (2023)
- [PPORLD-EDNetLDCT: A Proximal Policy Optimization-Based Reinforcement Learning Framework for Adaptive Low-Dose CT Denoising](https://mlsanity.com/paper/2509.03185/) (2025)
- [Latent Space Reinforcement Learning for Multi-Robot Exploration](https://mlsanity.com/paper/2601.01139/) (2026)
- [AdaFuse: Adaptive Multimodal Fusion for Lung Cancer Risk Prediction via Reinforcement Learning](https://mlsanity.com/paper/2602.00347/) (2026)

## 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.