# ESAD: Endoscopic Surgeon Action Detection Dataset

arXiv: https://arxiv.org/abs/2006.07164 · HTML: https://mlsanity.com/paper/2006.07164/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 11 of 11)

- [GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI](https://mlsanity.com/paper/2408.03361/) (2024)
- [LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching](https://mlsanity.com/paper/2306.11925/) (2023)
- [A Systematic Collection of Medical Image Datasets for Deep Learning](https://mlsanity.com/paper/2106.12864/) (2021)
- [Gesture Recognition in Robotic Surgery: a Review](https://mlsanity.com/paper/2102.00027/) (2021)
- [Instrument-tissue Interaction Detection Framework for Surgical Video Understanding](https://mlsanity.com/paper/2404.00322/) (2024)
- [Unsupervised anomaly detection for a Smart Autonomous Robotic Assistant Surgeon (SARAS)using a deep residual autoencoder](https://mlsanity.com/paper/2104.11008/) (2021)
- [Spatiotemporal Deformable Scene Graphs for Complex Activity Detection](https://mlsanity.com/paper/2104.08194/) (2021)
- [RIDE: Self-Supervised Learning of Rotation-Equivariant Keypoint Detection and Invariant Description for Endoscopy](https://mlsanity.com/paper/2309.09563/) (2023)
- [Artificial Intelligence for Biomedical Video Generation](https://mlsanity.com/paper/2411.07619/) (2024)
- [Deep Homography Estimation in Dynamic Surgical Scenes for Laparoscopic Camera Motion Extraction](https://mlsanity.com/paper/2109.15098/) (2021)
- [Spatiotemporal Event Graphs for Dynamic Scene Understanding](https://mlsanity.com/paper/2312.07621/) (2023)

## Abstract (arXiv)

In this work, we take aim towards increasing the effectiveness of surgical assistant robots. We intended to make assistant robots safer by making them aware about the actions of surgeon, so it can take appropriate assisting actions. In other words, we aim to solve the problem of surgeon action detection in endoscopic videos. To this, we introduce a challenging dataset for surgeon action detection in real-world endoscopic videos. Action classes are picked based on the feedback of surgeons and annotated by medical professional. Given a video frame, we draw bounding box around surgical tool which is performing action and label it with action label. Finally, we presenta frame-level action detection baseline model based on recent advances in ob-ject detection. Results on our new dataset show that our presented dataset provides enough interesting challenges for future method and it can serveas strong benchmark corresponding research in surgeon action detection in endoscopic videos.