SGT: Scene Graph-Guided Transformer for Surgical Report Generation

The robotic surgical report reflects the operations during surgery and relates to the subsequent treatment. Therefore, it is especially important to generate accurate surgical reports. Given that there are numerous interactions between instruments and tissue in the surgical scene, we propose a Scene Graph-guided Transformer (SGT) to solve the issue of surgical report generation. The model is based on the structure of transformer to understand the complex interactions between tissue and the instruments from both global and local perspectives. On the one hand, we propose a relation driven attention to facilitate the comprehensive description of the interaction in a generated report via sampling of numerous interactive relationships to form a diverse and representative augmented memory. On the other hand, to characterize the specific interactions in each surgical image, a simple yet ingenious approach is proposed for homogenizing the input heterogeneous scene graph, which plays an effective role in modeling the local interactions by injecting the graph-induced attention into the encoder. The dataset from clinical nephrectomy is utilized for performance evaluation and the experimental results show that our SGT model can significantly improve the quality of the generated surgical medical report, far exceeding the other state-of-the-art methods. The code is public available at: https://github.com ccccchenllll/SGT_master.