Phase recognition plays an essential role for surgical workflow analysis in
computer assisted intervention. Transformer, originally proposed for sequential
data modeling in natural language processing, has been successfully applied to
surgical phase recognition. Existing works based on transformer mainly focus on
modeling attention dependency, without introducing auto-regression. In this
work, an Auto-Regressive Surgical Transformer, referred as ARST, is first
proposed for on-line surgical phase recognition from laparoscopic videos,
modeling the inter-phase correlation implicitly by conditional probability
distribution. To reduce inference bias and to enhance phase consistency, we
further develop a consistency constraint inference strategy based on
auto-regression. We conduct comprehensive validations on a well-known public
dataset Cholec80. Experimental results show that our method outperforms the
state-of-the-art methods both quantitatively and qualitatively, and achieves an
inference rate of 66 frames per second (fps).

本文提出了一种名为 ARST 的自回归手术变压器，用于从腹腔镜视频中实时识别手术阶段。通过条件概率分布隐式地建模了阶段间相关性，并采用一致性约束推断策略以减少推断偏差和增强阶段一致性。在知名的公共数据集 Cholec80 上进行了综合验证，实验结果表明，我们的方法在定量和定性上都优于现有的最先进方法，并实现了每秒 66 帧的推断速率。