For robot-assisted surgery, an accurate surgical report reflects clinical operations during surgery and helps document entry tasks, post-operative analysis and follow-up treatment. It is a challenging task due to many complex and diverse interactions between instruments and tissues in the surgical scene. Although existing surgical report generation methods based on deep learning have achieved large success, they often ignore the interactive relation between tissues and instrumental tools, thereby degrading the report generation performance. This paper presents a neural network to boost surgical report generation by explicitly exploring the interactive relation between tissues and surgical instruments. We validate the effectiveness of our method on a widely-used robotic surgery benchmark dataset, and experimental results show that our network can significantly outperform existing state-of-the-art surgical report generation methods (e.g., 7.48% and 5.43% higher for BLEU-1 and ROUGE).

翻译：针对机器人辅助手术场景，准确的手术报告不仅反映术中临床操作，更有助于完善文书录入、术后分析及后续治疗。由于手术场景中器械与组织之间存在大量复杂多样的动态交互行为，该任务极具挑战性。尽管现有基于深度学习的手术报告生成方法已取得显著成效，但其往往忽视组织与手术器械之间的交互关系，导致报告生成性能受限。本文提出一种神经网络模型，通过显式探索组织与手术器械间的交互关系来提升手术报告生成质量。在广泛使用的机器人手术基准数据集上的实验结果表明，本方法显著优于现有最先进的手术报告生成方法（在BLEU-1和ROUGE指标上分别提升7.48%和5.43%）。