Computer-Assisted Intervention (CAI) has the potential to revolutionize modern surgery, with surgical scene understanding serving as a critical component in supporting decision-making, improving procedural efficacy, and ensuring intraoperative safety. While existing AI-driven approaches alleviate annotation burdens via self-supervised spatial representation learning, their lack of explicit temporal modeling during pre-training fundamentally restricts the capture of dynamic surgical contexts, resulting in incomplete spatiotemporal understanding. In this work, we introduce the first video-level surgical pre-training framework that enables joint spatiotemporal representation learning from large-scale surgical video data. To achieve this, we constructed a large-scale surgical video dataset comprising 3,650 videos and approximately 3.55 million frames, spanning more than 20 surgical procedures and over 10 anatomical structures. Building upon this dataset, we propose SurgVISTA (Surgical Video-level Spatial-Temporal Architecture), a reconstruction-based pre-training method that captures intricate spatial structures and temporal dynamics through joint spatiotemporal modeling. Additionally, SurgVISTA incorporates image-level knowledge distillation guided by a surgery-specific expert to enhance the learning of fine-grained anatomical and semantic features. To validate its effectiveness, we established a comprehensive benchmark comprising 13 video-level datasets spanning six surgical procedures across four tasks. Extensive experiments demonstrate that SurgVISTA consistently outperforms both natural- and surgical-domain pre-trained models, demonstrating strong potential to advance intelligent surgical systems in clinically meaningful scenarios.
View on arXiv@article{yang2025_2506.02692,
title={ Large-scale Self-supervised Video Foundation Model for Intelligent Surgery },
author={ Shu Yang and Fengtao Zhou and Leon Mayer and Fuxiang Huang and Yiliang Chen and Yihui Wang and Sunan He and Yuxiang Nie and Xi Wang and Ömer Sümer and Yueming Jin and Huihui Sun and Shuchang Xu and Alex Qinyang Liu and Zheng Li and Jing Qin and Jeremy YuenChun Teoh and Lena Maier-Hein and Hao Chen },
journal={arXiv preprint arXiv:2506.02692},
year={ 2025 }
}