# GynSurg: A Comprehensive Gynecology Laparoscopic Surgery Dataset

arXiv: https://arxiv.org/abs/2506.11356 · HTML: https://mlsanity.com/paper/2506.11356/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 12 Jun 2025.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [CE-Net: Context Encoder Network for 2D Medical Image Segmentation](https://mlsanity.com/paper/1903.02740/) (2019)
- [AutoLaparo: A New Dataset of Integrated Multi-tasks for Image-guided Surgical Automation in Laparoscopic Hysterectomy](https://mlsanity.com/paper/2208.02049/) (2022)
- [Video-based surgical skill assessment using 3D convolutional neural networks](https://mlsanity.com/paper/1903.02306/) (2019)
- [LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos](https://mlsanity.com/paper/2107.00875/) (2021)
- [ReCal-Net: Joint Region-Channel-Wise Calibrated Network for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2109.12448/) (2021)
- [DeepPyramid: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2207.01453/) (2022)
- [A semi-supervised Teacher-Student framework for surgical tool detection and localization](https://mlsanity.com/paper/2208.09926/) (2022)
- [Event Recognition in Laparoscopic Gynecology Videos with Hybrid Transformers](https://mlsanity.com/paper/2312.00593/) (2023)

## Built on by (most cited first, 8 of 8)

- [SUREON: A Benchmark and Vision-Language-Model for Surgical Reasoning](https://mlsanity.com/paper/2603.06570/) (2026)
- [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026)
- [LapFM: A Laparoscopic Segmentation Foundation Model via Hierarchical Concept Evolving Pre-training](https://mlsanity.com/paper/2512.08439/) (2025)
- [Cholec80-port: A Geometrically Consistent Trocar Port Segmentation Dataset for Robust Surgical Scene Understanding](https://mlsanity.com/paper/2602.17060/) (2026)
- [Data-Efficient Learning for Generalizable Surgical Video Understanding](https://mlsanity.com/paper/2508.10215/) (2025)
- [Fusionista2.0: Efficiency Retrieval System for Large-Scale Datasets](https://mlsanity.com/paper/2511.12255/) (2025)
- [Attribute Retrieving for Open-Vocabulary Endoscopic Compositional Referring Segmentation](https://mlsanity.com/paper/2607.08397/) (2026)
- [A Surgical Foundation Model Reveals Task-Dependent Label Efficiency](https://mlsanity.com/paper/2609.31821/) (2026)

## Abstract (arXiv)

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