Multi-agent reinforcement learning for prostate localization based on multi-scale image representation

The analysis of magnetic resonance (MR) images plays an important role in medicine diagnosis. The localization of the anatomical structure of lesions or organs is a very important pretreatment step in clinical treatment planning. Furthermore, the accuracy of localization directly affects the diagnosis. We propose a multi-agent deep reinforcement learning-based method for prostate localization in MR image. We construct a collaborative communication environment for multi-agent interaction by sharing parameters of convolution layers of all agents. Because each agent needs to make action strategies independently, the fully connected layers are separate for each agent. In addition, we present a coarse-to-fine multi-scale image representation method to further improve the accuracy of prostate localization. The experimental results show that our method outperforms several state- of-the-art methods on PROMISE12 test dataset.