This paper presents an approach for surgical phase recognition using video
data, aiming to provide a comprehensive understanding of surgical procedures
for automated workflow analysis. The advent of robotic surgery, digitized
operating rooms, and the generation of vast amounts of data have opened doors
for the application of machine learning and computer vision in the analysis of
surgical videos. Among these advancements, Surgical Phase Recognition(SPR)
stands out as an emerging technology that has the potential to recognize and
assess the ongoing surgical scenario, summarize the surgery, evaluate surgical
skills, offer surgical decision support, and facilitate medical training. In
this paper, we analyse and evaluate both frame-based and video clipping-based
phase recognition on thoracic surgery dataset consisting of 11 classes of
phases. Specifically, we utilize ImageNet ViT for image-based classification
and VideoMAE as the baseline model for video-based classification. We show that
Masked Video Distillation(MVD) exhibits superior performance, achieving a top-1
accuracy of 72.9%, compared to 52.31% achieved by ImageNet ViT. These findings
underscore the efficacy of video-based classifiers over their image-based
counterparts in surgical phase recognition tasks.

该研究论文介绍了一种利用视频数据进行手术阶段识别的方法，旨在为自动化工作流分析提供手术程序的全面理解。研究表明，在手术阶段识别任务中，视频基分类器相比于基于图片的分类器具有更高的效果。