Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images

2020-08-18 · Guy Leroy, Daniel Rueckert, Amir Alansary

Accurate detection of anatomical landmarks is an essential step in several medical imaging tasks. We propose a novel communicative multi-agent reinforcement learning (C-MARL) system to automatically detect landmarks in 3D brain images. C-MARL enables the agents to learn explicit communication channels, as well as implicit communication signals by sharing certain weights of the architecture among all the agents. The proposed approach is evaluated on two brain imaging datasets from adult magnetic resonance imaging (MRI) and fetal ultrasound scans. Our experiments show that involving multiple cooperating agents by learning their communication with each other outperforms previous approaches using single agents.

📄 PDF Abstract BibTeX arXiv:2008.08055

Code (1)

gml16/rl-medical 공식 구현 tf

Tasks

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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