Exploiting the potential of unlabeled endoscopic video data with self-supervised learning

Understand

Surgical data science is a new research field that aims to observe all aspects of the patient treatment process in order to provide the right assistance at the right time.

• Due to the breakthrough successes of deep learning-based solutions for automatic image annotation, the availability of reference annotations for algorithm training is becoming a major bottleneck in the field.
• The purpose of this paper was to investigate the concept of self-supervised learning to address this issue.
• Our approach is guided by the hypothesis that unlabeled video data can be used to learn a representation of the target domain that boosts the performance of state-of-the-art machine learning algorithms when used for pre-training.