  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).
