论文标题
在大脑图像中进行具有里程碑意义的检测的沟通加强学习剂
Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images
论文作者
论文摘要
准确检测解剖学地标是几个医学成像任务中的重要步骤。我们提出了一种新型的交流多代理增强学习（C-MARL）系统，以自动检测3D脑图像中的地标。 C-MARL使代理商可以通过在所有代理之间共享架构的某些权重来学习明确的通信渠道以及隐式通信信号。从成人磁共振成像（MRI）和胎儿超声扫描中评估了两个脑成像数据集的建议方法。我们的实验表明，涉及多个合作代理人相互学习的沟通优于以前的方法，使用单个代理。
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.
