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。

ARST: 从腹腔镜视频中实现术前阶段识别的自回归手术变压器

ARST: Auto-Regressive Surgical Transformer for Phase Recognition from Laparoscopic Videos

Accurate surgery duration estimation is necessary for optimal OR planning,
which plays an important role in patient comfort and safety as well as resource
optimization. It is, however, challenging to preoperatively predict surgery
duration since it varies significantly depending on the patient condition,
surgeon skills, and intraoperative situation. In this paper, we propose a deep
learning pipeline, referred to as RSDNet, which automatically estimates the
remaining surgery duration (RSD) intraoperatively by using only visual
information from laparoscopic videos. Previous state-of-the-art approaches for
RSD prediction are dependent on manual annotation, whose generation requires
expensive expert knowledge and is time-consuming, especially considering the
numerous types of surgeries performed in a hospital and the large number of
laparoscopic videos available. A crucial feature of RSDNet is that it does not
depend on any manual annotation during training, making it easily scalable to
many kinds of surgeries. The generalizability of our approach is demonstrated
by testing the pipeline on two large datasets containing different types of
surgeries: 120 cholecystectomy and 170 gastric bypass videos. The experimental
results also show that the proposed network significantly outperforms a
traditional method of estimating RSD without utilizing manual annotation.
Further, this work provides a deeper insight into the deep learning network
through visualization and interpretation of the features that are automatically
learned.

本文提出名为 RSDNet 的深度学习管道，通过仅使用腹腔镜视频的视觉信息自动预测术中剩余手术时间 (RSD)，无需手动标注，可适用于多种手术类型，实验结果表明相比不使用手动标注的传统方法，该网络显著提高了 RSD 预测的准确性，并可通过可视化和解释自动学习的特征提供更深入的洞察。