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

• URL: http://arxiv.org/abs/2506.07603v2
• Date: Mon, 16 Jun 2025 03:31:25 GMT
• Title: SurgBench: A Unified Large-Scale Benchmark for Surgical Video Analysis
• Authors: Jianhui Wei, Zikai Xiao, Danyu Sun, Luqi Gong, Zongxin Yang, Zuozhu Liu, Jian Wu,
• Abstract summary: SurgBench is a unified surgical video benchmarking framework comprising a pretraining dataset, textbfSurgBench-P, and an evaluation benchmark, textbfSurgBench-E.<n>SurgBench-P covers 53 million frames across 22 surgical procedures and 11 specialties, and SurgBench-E provides robust evaluation across six categories (phase classification, camera motion, tool recognition, disease diagnosis, action classification, and organ detection) spanning 72 fine-grained tasks.
• Score: 20.566701996432226
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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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