  Endoscopic video recordings are widely used in minimally invasive
robot-assisted surgery, but when the endoscope is outside the patient's body,
it can capture irrelevant segments that may contain sensitive information. To
address this, we propose a framework that accurately detects out-of-body frames
in surgical videos by leveraging self-supervision with minimal data labels. We
use a massive amount of unlabeled endoscopic images to learn meaningful
representations in a self-supervised manner. Our approach, which involves
pre-training on an auxiliary task and fine-tuning with limited supervision,
outperforms previous methods for detecting out-of-body frames in surgical
videos captured from da Vinci X and Xi surgical systems. The average F1 scores
range from 96.00 to 98.02. Remarkably, using only 5% of the training labels,
our approach still maintains an average F1 score performance above 97,
outperforming fully-supervised methods with 95% fewer labels. These results
demonstrate the potential of our framework to facilitate the safe handling of
surgical video recordings and enhance data privacy protection in minimally
invasive surgery.
