@inproceedings{alansary2018evaluating,
  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 Glocker, Ben and Kainz, Bernhard and Rueckert, Daniel},
  booktitle={ Medical Imaging with Deep Learning},
  year={2018},
  organization={Amsterdam},
  doi = {10.1016/j.media.2019.02.007},
  url = {https://doi.org/10.1016/j.media.2019.02.007},
  code = {https://github.com/amiralansary/rl-medical},
  abstract = {Automatic detection of anatomical landmarks is essential for many medical-image-analysis and acquisition methods, while manual annotation is time-consuming and error-prone. This work evaluates reinforcement-learning strategies that train agents to localize landmarks in medical scans, including fixed- and multiscale search with hierarchical coarse-to-fine actions. Several deep Q-network architectures are tested on a challenging fetal-head-ultrasound dataset.},
}