  Vision algorithms capable of interpreting scenes from a real-time video
stream are necessary for computer-assisted surgery systems to achieve
context-aware behavior. In laparoscopic procedures one particular algorithm
needed for such systems is the identification of surgical phases, for which the
current state of the art is a model based on a CNN-LSTM. A number of previous
works using models of this kind have trained them in a fully supervised manner,
requiring a fully annotated dataset. Instead, our work confronts the problem of
learning surgical phase recognition in scenarios presenting scarce amounts of
annotated data (under 25% of all available video recordings). We propose a
teacher/student type of approach, where a strong predictor called the teacher,
trained beforehand on a small dataset of ground truth-annotated videos,
generates synthetic annotations for a larger dataset, which another model - the
student - learns from. In our case, the teacher features a novel CNN-biLSTM-CRF
architecture, designed for offline inference only. The student, on the other
hand, is a CNN-LSTM capable of making real-time predictions. Results for
various amounts of manually annotated videos demonstrate the superiority of the
new CNN-biLSTM-CRF predictor as well as improved performance from the CNN-LSTM
trained using synthetic labels generated for unannotated videos. For both
offline and online surgical phase recognition with very few annotated
recordings available, this new teacher/student strategy provides a valuable
performance improvement by efficiently leveraging the unannotated data.
