GynSurg: A Comprehensive Gynecology Laparoscopic Surgery Dataset
This addresses the problem of limited data for surgical video analysis in gynecology, enabling better computer-assisted interventions, though it is incremental as it builds on existing dataset efforts.
The authors tackled the lack of large, high-quality annotated datasets for gynecologic laparoscopic surgery by introducing GynSurg, the largest and most diverse multi-task dataset to date, which they demonstrated by benchmarking state-of-the-art models under a standardized protocol.
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