  Automatic surgical workflow recognition in video is an essentially
fundamental yet challenging problem for developing computer-assisted and
robotic-assisted surgery. Existing approaches with deep learning have achieved
remarkable performance on analysis of surgical videos, however, heavily relying
on large-scale labelled datasets. Unfortunately, the annotation is not often
available in abundance, because it requires the domain knowledge of surgeons.
In this paper, we propose a novel active learning method for cost-effective
surgical video analysis. Specifically, we propose a non-local recurrent
convolutional network (NL-RCNet), which introduces non-local block to capture
the long-range temporal dependency (LRTD) among continuous frames. We then
formulate an intra-clip dependency score to represent the overall dependency
within this clip. By ranking scores among clips in unlabelled data pool, we
select the clips with weak dependencies to annotate, which indicates the most
informative ones to better benefit network training. We validate our approach
on a large surgical video dataset (Cholec80) by performing surgical workflow
recognition task. By using our LRTD based selection strategy, we can outperform
other state-of-the-art active learning methods. Using only up to 50% of
samples, our approach can exceed the performance of full-data training.
