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SurgBench: A Unified Large-Scale Benchmark for Surgical Video Analysis

9 June 2025
Jianhui Wei
Zikai Xiao
Danyu Sun
Luqi Gong
Zongxin Yang
Zuozhu Liu
Jian Wu
ArXiv (abs)PDFHTML
Main:8 Pages
9 Figures
Bibliography:3 Pages
8 Tables
Appendix:8 Pages
Abstract

Surgical video understanding is pivotal for enabling automated intraoperative decision-making, skill assessment, and postoperative quality improvement. However, progress in developing surgical video foundation models (FMs) remains hindered by the scarcity of large-scale, diverse datasets for pretraining and systematic evaluation. In this paper, we introduce \textbf{SurgBench}, a unified surgical video benchmarking framework comprising a pretraining dataset, \textbf{SurgBench-P}, and an evaluation benchmark, \textbf{SurgBench-E}. SurgBench offers extensive coverage of diverse surgical scenarios, with SurgBench-P encompassing 53 million frames across 22 surgical procedures and 11 specialties, and SurgBench-E providing robust evaluation across six categories (phase classification, camera motion, tool recognition, disease diagnosis, action classification, and organ detection) spanning 72 fine-grained tasks. Extensive experiments reveal that existing video FMs struggle to generalize across varied surgical video analysis tasks, whereas pretraining on SurgBench-P yields substantial performance improvements and superior cross-domain generalization to unseen procedures and modalities. Our dataset and code are available upon request.

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@article{wei2025_2506.07603,
  title={ SurgBench: A Unified Large-Scale Benchmark for Surgical Video Analysis },
  author={ Jianhui Wei and Zikai Xiao and Danyu Sun and Luqi Gong and Zongxin Yang and Zuozhu Liu and Jian Wu },
  journal={arXiv preprint arXiv:2506.07603},
  year={ 2025 }
}
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