This paper explores how graph neural networks (GNNs) can be used to enhance
visual scene understanding and surgical skill assessment. By using GNNs to
analyze the complex visual data of surgical procedures represented as graph
structures, relevant features can be extracted and surgical skill can be
predicted. Additionally, GNNs provide interpretable results, revealing the
specific actions, instruments, or anatomical structures that contribute to the
predicted skill metrics. This can be highly beneficial for surgical educators
and trainees, as it provides valuable insights into the factors that contribute
to successful surgical performance and outcomes. SurGNN proposes two concurrent
approaches -- one supervised and the other self-supervised. The paper also
briefly discusses other automated surgical skill evaluation techniques and
highlights the limitations of hand-crafted features in capturing the
intricacies of surgical expertise. We use the proposed methods to achieve
state-of-the-art results on EndoVis19, and custom datasets. The working
implementation of the code can be found at this https URL>.

该论文研究了图神经网络如何增强视觉场景理解和手术技能评估，通过使用图神经网络分析以图形结构表示的手术过程的复杂视觉数据，可以提取相关特征并预测手术技能。此外，图神经网络提供可解释的结果，揭示了对预测技能指标有贡献的具体行动、仪器或解剖结构，这对于手术教育者和学员非常有益，因为它提供了成功手术表现和结果的重要见解。SurGNN 提出了两种并行的方法 - 一种是有监督的，另一种是自监督的。文中还简要讨论了其他自动化手术技能评估技术，并强调手工设计特征在捕捉手术专长的细微差别方面的局限性。我们使用所提出的方法在 EndoVis19 和自定义数据集上达到了最先进的结果。代码的工作实现可以在此 https URL 找到。