论文标题
概念图神经网络，用于手术视频理解
Concept Graph Neural Networks for Surgical Video Understanding
论文作者
论文摘要
我们不断整合对世界的知识和理解，以增强我们对所见事物的解释。 这种能力在应用领域至关重要，这些应用领域需要推理多个实体和概念，例如AI仪器手术。在本文中，我们提出了一种新颖的方式，将概念知识通过时间概念图网络纳入时间分析任务。在拟议的网络中，将全球知识图纳入了手术实例的时间分析中，学习了概念和关系的含义，它们适用于数据。我们在手术视频数据中展示了我们的结果，以进行诸如核心安全视图的验证以及对Parkland评分量表的估计。结果表明，我们的方法改善了复杂基准的识别和检测，并实现了其他关注的分析应用。
We constantly integrate our knowledge and understanding of the world to enhance our interpretation of what we see. This ability is crucial in application domains which entail reasoning about multiple entities and concepts, such as AI-augmented surgery. In this paper, we propose a novel way of integrating conceptual knowledge into temporal analysis tasks via temporal concept graph networks. In the proposed networks, a global knowledge graph is incorporated into the temporal analysis of surgical instances, learning the meaning of concepts and relations as they apply to the data. We demonstrate our results in surgical video data for tasks such as verification of critical view of safety, as well as estimation of Parkland grading scale. The results show that our method improves the recognition and detection of complex benchmarks as well as enables other analytic applications of interest.
