构建首个大规模妇科腹腔镜手术多任务数据集，助力智能手术分析
GynSurg: A Comprehensive Gynecology Laparoscopic Surgery Dataset
- 收集并标注多任务数据，涵盖动作识别与语义分割
- 覆盖超100小时手术视频，支持端到端流程分析
- 适合医疗AI研究者与手术智能化开发者使用
深度学习的进展已推动计算机辅助干预和手术视频分析的变革，显著提升了外科培训、术中决策支持、患者预后，以及术后记录与手术发现。这些进展依赖于大规模高质量的标注数据集。在妇科腹腔镜手术中，手术场景理解与动作识别是构建智能辅助系统的基础。然而，现有数据集普遍存在规模小、任务单一、标注不充分等问题，限制了全面的端到端流程分析。为此，我们推出了目前最大、最多样化的妇科腹腔镜手术多任务数据集——GynSurg。该数据集提供丰富标注，支持动作识别、语义分割、手术文档生成及新手术模式发现等多种应用。通过标准化训练协议评估先进模型，验证了数据集的质量与泛化能力。为加速领域发展，我们公开发布GynSurg数据集及其完整标注。
原文摘要 · Abstract (English)
Recent advances in deep learning have transformed computer-assisted intervention and surgical video analysis, driving improvements not only in surgical training, intraoperative decision support, and patient outcomes, but also in postoperative documentation and surgical discovery. Central to these developments is the availability of large, high-quality annotated datasets. In gynecologic laparoscopy, surgical scene understanding and action recognition are fundamental for building intelligent systems that assist surgeons during operations and provide deeper analysis after surgery. However, existing datasets are often limited by small scale, narrow task focus, or insufficiently detailed annotations, limiting their utility for comprehensive, end-to-end workflow analysis. To address these limitations, we introduce GynSurg, the largest and most diverse multi-task dataset for gynecologic laparoscopic surgery to date. GynSurg provides rich annotations across multiple tasks, supporting applications in action recognition, semantic segmentation, surgical documentation, and discovery of novel procedural insights. We demonstrate the dataset quality and versatility by benchmarking state-of-the-art models under a standardized training protocol. To accelerate progress in the field, we publicly release the GynSurg dataset and its annotations
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