Large-scale Self-supervised Video Foundation Model for Intelligent Surgery

• URL: http://arxiv.org/abs/2506.02692v1
• Date: Tue, 03 Jun 2025 09:42:54 GMT
• Title: Large-scale Self-supervised Video Foundation Model for Intelligent Surgery
• Authors: Shu Yang, Fengtao Zhou, Leon Mayer, Fuxiang Huang, Yiliang Chen, Yihui Wang, Sunan He, Yuxiang Nie, Xi Wang, Ömer Sümer, Yueming Jin, Huihui Sun, Shuchang Xu, Alex Qinyang Liu, Zheng Li, Jing Qin, Jeremy YuenChun Teoh, Lena Maier-Hein, Hao Chen,
• Abstract summary: We introduce the first video-level surgical pre-training framework that enables jointtemporal representation learning from large-scale surgical video data.<n>We propose SurgVISTA, a reconstruction-based pre-training method that captures spatial structures and intricate temporal dynamics.<n>In experiments, SurgVISTA consistently outperforms both natural- and surgical-domain pre-trained models.
• Score: 27.418249899272155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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