Summary:

This study investigates the use of self-supervised learning in surgical computer vision to overcome data annotation challenges. By leveraging diverse surgical datasets and pre-training models, the research explores different pre-training dataset combinations and their impact on downstream task performance in surgical recognition and safety assessment. The findings highlight significant performance improvements across various tasks, emphasizing the importance of dataset composition in self-supervised learning methodologies.

• Examines self-supervised learning in surgical computer vision.
• Investigates the impact of dataset composition on downstream tasks.
• Highlights performance boosts with diverse datasets.
• Importance and Future Research: The study sheds light on the effectiveness of self-supervised learning in leveraging diverse datasets for surgical tasks, paving the way for further research on dataset optimization for improved model performance.