To enable context-aware computer assistance in the operating room of the
future, cognitive systems need to understand automatically which surgical phase
is being performed by the medical team. The primary source of information for
surgical phase recognition is typically video, which presents two challenges:
extracting meaningful features from the video stream and effectively modeling
temporal information in the sequence of visual features. For temporal modeling,
attention mechanisms have gained popularity due to their ability to capture
long-range dependencies. In this paper, we explore design choices for attention
in existing temporal models for surgical phase recognition and propose a novel
approach that does not resort to local attention or regularization of attention
weights: TUNeS is an efficient and simple temporal model that incorporates
self-attention at the coarsest stage of a U-Net-like structure. In addition, we
propose to train the feature extractor, a standard CNN, together with an LSTM
on preferably long video segments, i.e., with long temporal context. In our
experiments, all temporal models performed better on top of feature extractors
that were trained with longer temporal context. On top of these contextualized
features, TUNeS achieves state-of-the-art results on Cholec80.

通过 TUNeS 模型，结合上下文感知的计算机辅助、注意力机制和时序建模，可以在未来手术室中实现自动识别医疗团队正在执行的手术阶段，并在考虑长期时序特征的情况下达到 Cholec80 数据集上的最佳结果。