  Generating a surgical report in robot-assisted surgery, in the form of
natural language expression of surgical scene understanding, can play a
significant role in document entry tasks, surgical training, and post-operative
analysis. Despite the state-of-the-art accuracy of the deep learning algorithm,
the deployment performance often drops when applied to the Target Domain (TD)
data. For this purpose, we develop a multi-layer transformer-based model with
the gradient reversal adversarial learning to generate a caption for the
multi-domain surgical images that can describe the semantic relationship
between instruments and surgical Region of Interest (ROI). In the gradient
reversal adversarial learning scheme, the gradient multiplies with a negative
constant and updates adversarially in backward propagation, discriminating
between the source and target domains and emerging domain-invariant features.
We also investigate model calibration with label smoothing technique and the
effect of a well-calibrated model for the penultimate layer's feature
representation and Domain Adaptation (DA). We annotate two robotic surgery
datasets of MICCAI robotic scene segmentation and Transoral Robotic Surgery
(TORS) with the captions of procedures and empirically show that our proposed
method improves the performance in both source and target domain surgical
reports generation in the manners of unsupervised, zero-shot, one-shot, and
few-shot learning.
