A reinforcement learning application of a guided Monte Carlo Tree Search algorithm for beam orientation selection in radiation therapy

@article{SadeghnejadBarkousaraie2021ARL,
  title={A reinforcement learning application of a guided Monte Carlo Tree Search algorithm for beam orientation selection in radiation therapy},
  author={Azar Sadeghnejad-Barkousaraie and Gyanendra Bohara and Steve B. Jiang and Dan Nguyen},
  journal={Machine Learning: Science and Technology},
  year={2021},
  volume={2}
}
Current beam orientation optimization algorithms for radiotherapy, such as column generation (CG), are typically heuristic or greedy in nature because of the size of the combinatorial problem, which leads to suboptimal solutions. We propose a reinforcement learning strategy using a Monte Carlo Tree Search (MCTS) that can find a better beam orientation set in less time than CG. We utilize a reinforcement learning structure involving a supervised learning network to guide the MCTS and to explore… 

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    Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics
  • 2021

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