# Multiple Landmark Detection using Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/1907.00318 · HTML: https://mlsanity.com/paper/1907.00318/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2019.

## Built on by (most cited first, 23 of 23)

- [Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey](https://mlsanity.com/paper/2108.11510/) (2021)
- [Deep reinforcement learning in medical imaging: A literature review](https://mlsanity.com/paper/2103.05115/) (2021)
- [Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net](https://mlsanity.com/paper/2106.10528/) (2021)
- [Reinforcement Learning in Medical Image Analysis: Concepts, Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2206.14302/) (2022)
- [Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound](https://mlsanity.com/paper/2105.10626/) (2021)
- [Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging](https://mlsanity.com/paper/2302.11510/) (2023)
- [U-LanD: Uncertainty-Driven Video Landmark Detection](https://mlsanity.com/paper/2102.01586/) (2021)
- [Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges](https://mlsanity.com/paper/2305.10091/) (2023)
- [Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound](https://mlsanity.com/paper/2503.20685/) (2025)
- [Next-Gen Machine Learning Supported Diagnostic Systems for Spacecraft](https://mlsanity.com/paper/2106.05659/) (2021)
- [Facial Anatomical Landmark Detection using Regularized Transfer Learning with Application to Fetal Alcohol Syndrome Recognition](https://mlsanity.com/paper/2109.05485/) (2021)
- [Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior](https://mlsanity.com/paper/2301.05392/) (2023)
- [Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging](https://mlsanity.com/paper/2303.06783/) (2023)
- [Active Sampling for MRI-based Sequential Decision Making](https://mlsanity.com/paper/2505.04586/) (2025)
- [SurgTEMP: Temporal-Aware Surgical Video Question Answering with Text-guided Visual Memory for Laparoscopic Cholecystectomy](https://mlsanity.com/paper/2603.29962/) (2026)
- [Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment](https://mlsanity.com/paper/2107.12800/) (2021)
- [Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style Transfer](https://mlsanity.com/paper/2111.03485/) (2021)
- [Coordinated Transformer with Position & Sample-aware Central Loss for Anatomical Landmark Detection](https://mlsanity.com/paper/2305.11338/) (2023)
- [Multi-environment lifelong deep reinforcement learning for medical imaging](https://mlsanity.com/paper/2306.00188/) (2023)
- [A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments](https://mlsanity.com/paper/2306.05310/) (2023)
- [Only My Model On My Data: A Privacy Preserving Approach Protecting one Model and Deceiving Unauthorized Black-Box Models](https://mlsanity.com/paper/2402.09316/) (2024)
- [Promptable segmentation with region exploration enables minimal-effort expert-level prostate cancer delineation](https://mlsanity.com/paper/2602.17813/) (2026)
- [Interactive Whole Slide Images for RL-based Tumour Segmentation](https://mlsanity.com/paper/2608.16607/) (2026)

## Abstract (arXiv)

The detection of anatomical landmarks is a vital step for medical image analysis and applications for diagnosis, interpretation and guidance. Manual annotation of landmarks is a tedious process that requires domain-specific expertise and introduces inter-observer variability. This paper proposes a new detection approach for multiple landmarks based on multi-agent reinforcement learning. Our hypothesis is that the position of all anatomical landmarks is interdependent and non-random within the human anatomy, thus finding one landmark can help to deduce the location of others. Using a Deep Q-Network (DQN) architecture we construct an environment and agent with implicit inter-communication such that we can accommodate K agents acting and learning simultaneously, while they attempt to detect K different landmarks. During training the agents collaborate by sharing their accumulated knowledge for a collective gain. We compare our approach with state-of-the-art architectures and achieve significantly better accuracy by reducing the detection error by 50%, while requiring fewer computational resources and time to train compared to the naive approach of training K agents separately.