论文标题
ARST：腹腔镜视频的自动回归手术变压器用于相位识别
ARST: Auto-Regressive Surgical Transformer for Phase Recognition from Laparoscopic Videos
论文作者
论文摘要
相位识别在计算机辅助干预中的手术工作流程分析中起着至关重要的作用。最初建议在自然语言处理中进行顺序数据建模的变压器已成功应用于手术阶段识别。基于变压器的现有作品主要集中于建模注意力依赖性，而无需引入自动回归。在这项工作中，首先提出了一种自动回归手术变压器（称为ARST），用于腹腔镜视频中的在线手术期识别，通过条件概率分布隐含地模拟了相之间的相关性。为了减少推理偏差并提高阶段一致性，我们进一步基于自动回归制定了一致性约束推理策略。我们对著名的公共数据集Cholec80进行全面验证。实验结果表明，我们的方法在定量和质量上都优于最新方法，并达到每秒66帧（FPS）的推理率。
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).
