  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.
