论文标题
通过强化学习和树木搜索，在机器人辅助手术中的自动姿势识别
Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search
论文作者
论文摘要
自动手术手势识别对于改善机器人辅助手术的智力至关重要，例如执行复杂的手术监测任务和技能评估。但是，当前方法可以单独处理每个框架，并在未来信息有效考虑的情况下产生结果。在本文中，我们提出了一个基于强化学习和树木搜索联合手术手势分割和分类的框架。对代理商进行了培训，可以以类似人类的方式进行细分和分类外科视频，其直接决策被适当地通过树木搜索重新考虑。我们提出的树搜索算法将来自两个设计的神经网络（即策略和价值网络）的输出组合在一起。随着来自不同模型的互补信息的整合，我们的框架能够比使用任何一个神经网络的基线方法实现更好的性能。为了进行整体评估，我们开发的方法在准确性，编辑分数和F1分数方面始终优于拼图数据集的缝合任务的现有方法。我们的研究强调了树木搜索的利用来完善对手术机器人应用的增强学习框架的作用。
Automatic surgical gesture recognition is fundamental for improving intelligence in robot-assisted surgery, such as conducting complicated tasks of surgery surveillance and skill evaluation. However, current methods treat each frame individually and produce the outcomes without effective consideration on future information. In this paper, we propose a framework based on reinforcement learning and tree search for joint surgical gesture segmentation and classification. An agent is trained to segment and classify the surgical video in a human-like manner whose direct decisions are re-considered by tree search appropriately. Our proposed tree search algorithm unites the outputs from two designed neural networks, i.e., policy and value network. With the integration of complementary information from distinct models, our framework is able to achieve the better performance than baseline methods using either of the neural networks. For an overall evaluation, our developed approach consistently outperforms the existing methods on the suturing task of JIGSAWS dataset in terms of accuracy, edit score and F1 score. Our study highlights the utilization of tree search to refine actions in reinforcement learning framework for surgical robotic applications.
