RL-Medical
Deep Reinforcement Learning (DRL) agents applied to medical images
Examples
- Landmark detection using different DQN variants for a single agent implemented using Tensorpack
- Landmark detection for multiple agents using different communication variants implemented in PyTorch
- Automatic view planning using different DQN variants
Installation
Dependencies
tensorpack-medical requires:
User installation
pip install -U git+https://github.com/amiralansary/rl-medical.git
Development
New contributors of any experience level are very welcomed
Source code
You can clone the latest version of the source code with the command::
https://github.com/amiralansary/rl-medical.git
Citation
If you use this code in your research, please cite these paper:
@article{alansary2019evaluating,
title={{Evaluating Reinforcement Learning Agents for Anatomical Landmark Detection}},
author={Alansary, Amir and Oktay, Ozan and Li, Yuanwei and Le Folgoc, Loic and
Hou, Benjamin and Vaillant, Ghislain and Kamnitsas, Konstantinos and
Vlontzos, Athanasios and Glocker, Ben and Kainz, Bernhard and Rueckert, Daniel},
journal={Medical Image Analysis},
year={2019},
publisher={Elsevier}
}
@inproceedings{alansary2018automatic,
title={Automatic view planning with multi-scale deep reinforcement learning agents},
author={Alansary, Amir and Le Folgoc, Loic and Vaillant, Ghislain and Oktay, Ozan and Li, Yuanwei and
Bai, Wenjia and Passerat-Palmbach, Jonathan and Guerrero, Ricardo and Kamnitsas, Konstantinos and Hou, Benjamin and others},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={277--285},
year={2018},
organization={Springer}
}
