  Learning to infer graph representations and performing spatial reasoning in a
complex surgical environment can play a vital role in surgical scene
understanding in robotic surgery. For this purpose, we develop an approach to
generate the scene graph and predict surgical interactions between instruments
and surgical region of interest (ROI) during robot-assisted surgery. We design
an attention link function and integrate with a graph parsing network to
recognize the surgical interactions. To embed each node with corresponding
neighbouring node features, we further incorporate SageConv into the network.
The scene graph generation and active edge classification mostly depend on the
embedding or feature extraction of node and edge features from complex image
representation. Here, we empirically demonstrate the feature extraction methods
by employing label smoothing weighted loss. Smoothing the hard label can avoid
the over-confident prediction of the model and enhances the feature
representation learned by the penultimate layer. To obtain the graph scene
label, we annotate the bounding box and the instrument-ROI interactions on the
robotic scene segmentation challenge 2018 dataset with an experienced clinical
expert in robotic surgery and employ it to evaluate our propositions.
