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).

本研究提出了一种神经网络方法，通过明确探索组织和手术器械之间的互动关系来提高手术报告的生成，实验结果表明我们的方法可以显著优于现有最先进的手术报告生成方法。