Multi-objective optimization of radiotherapy: distributed Q-learning and agent-based simulation

By A. Jalalimanesh, H. Haghighi, Abbas Ahmadi, Hossein Hejazian, M. Soltani. 2017

34 citations recorded by the source corpus.

Subfields: Modeling and Simulation, Radiation, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Modeling and Simulation

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu

In Radiation

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu

In Radiology, Nuclear Medicine and Imaging

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, methodology): “for some tissue types. As the goal of radiotherapy is in essence a multi-objective problem to erase the tumour with radiation while not impacting normal cells as much as possible, Jalalimanesh et al. [95] proposed a multi-objective distributed Q-learning algorithm to find the Pareto-optimal solutions”