A key element of computer-assisted surgery systems is phase recognition of surgical videos. Existing phase recognition algorithms require frame-wise annotation of a large number of videos, which is time and money consuming. In this work we join concepts of graph segmentation with self-supervised learning to derive a random-walk solution for per-frame phase prediction. Furthermore, we utilize within our method two forms of weak supervision: sparse timestamps or few-shot learning. The proposed algorithm enjoys low complexity and can operate in lowdata regimes. We validate our method by running experiments with the public Cholec80 dataset of laparoscopic cholecystectomy videos, demonstrating promising performance in multiple setups.

翻译：计算机辅助手术系统的关键要素之一是手术视频的阶段识别。现有阶段识别算法需要对大量视频进行逐帧标注，这既耗时又耗费资金。本研究将图分割概念与自监督学习相结合，推导出一种用于逐帧阶段预测的随机游走解法。此外，我们在方法中利用了两种形式的弱监督：稀疏时间戳或小样本学习。所提算法复杂度低，且能在低数据量场景下运行。我们通过使用公开的腹腔镜胆囊切除术视频数据集Cholec80进行实验来验证该方法，实验结果表明其在多种设置下均展现出令人满意的性能。