  Prior work demonstrated the ability of machine learning to automatically
recognize surgical workflow steps from videos. However, these studies focused
on only a single type of procedure. In this work, we analyze, for the first
time, surgical step recognition on four different laparoscopic surgeries:
Cholecystectomy, Right Hemicolectomy, Sleeve Gastrectomy, and Appendectomy.
Inspired by the traditional apprenticeship model, in which surgical training is
based on the Halstedian method, we paraphrase the "see one, do one, teach one"
approach for the surgical intelligence domain as "train one, classify one,
teach one". In machine learning, this approach is often referred to as transfer
learning. To analyze the impact of transfer learning across different
laparoscopic procedures, we explore various time-series architectures and
examine their performance on each target domain. We introduce a new
architecture, the Time-Series Adaptation Network (TSAN), an architecture
optimized for transfer learning of surgical step recognition, and we show how
TSAN can be pre-trained using self-supervised learning on a Sequence Sorting
task. Such pre-training enables TSAN to learn workflow steps of a new
laparoscopic procedure type from only a small number of labeled samples from
the target procedure. Our proposed architecture leads to better performance
compared to other possible architectures, reaching over 90% accuracy when
transferring from laparoscopic Cholecystectomy to the other three procedure
types.
