A Surgical Foundation Model Reveals Task-Dependent Label Efficiency
This study addresses the label efficiency bottleneck in surgical AI caused by the scarcity and high cost of expert annotations by proposing SURGE. The model is pretrained via self-supervised learning on over 30 million frames of surgical video, and its label efficiency is systematically evaluated across multiple benchmark tasks. The research reveals task-dependent label scaling laws, providing a theoretical blueprint for the efficient allocation of expert annotation resources in complex medical scenarios. Experimental results demonstrate that SURGE outperforms existing state-of-the-art methods across all tested benchmarks, surpassing even specialized models on complex reasoning tasks. Consequently, this work significantly enhances the few-shot generalization capability of surgical AI systems.