# Action Recognition in Video Recordings from Gynecologic Laparoscopy

arXiv: https://arxiv.org/abs/2311.18666 · HTML: https://mlsanity.com/paper/2311.18666/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis](https://mlsanity.com/paper/1907.06099/) (2019)
- [The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods](https://mlsanity.com/paper/2104.03178/) (2021)
- [m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural Networks](https://mlsanity.com/paper/2008.10134/) (2020)
- [Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy](https://mlsanity.com/paper/1706.07002/) (2017)
- [A Smoke Removal Method for Laparoscopic Images](https://mlsanity.com/paper/1803.08410/) (2018)

## Built on by (most cited first, 4 of 4)

- [Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase Recognition, and Irregularity Detection](https://mlsanity.com/paper/2312.06295/) (2023)
- [Event Recognition in Laparoscopic Gynecology Videos with Hybrid Transformers](https://mlsanity.com/paper/2312.00593/) (2023)
- [Dual Invariance Self-training for Reliable Semi-supervised Surgical Phase Recognition](https://mlsanity.com/paper/2501.17628/) (2025)
- [Data-Efficient Learning for Generalizable Surgical Video Understanding](https://mlsanity.com/paper/2508.10215/) (2025)

## Abstract (arXiv)

Action recognition is a prerequisite for many applications in laparoscopic video analysis including but not limited to surgical training, operation room planning, follow-up surgery preparation, post-operative surgical assessment, and surgical outcome estimation. However, automatic action recognition in laparoscopic surgeries involves numerous challenges such as (I) cross-action and intra-action duration variation, (II) relevant content distortion due to smoke, blood accumulation, fast camera motions, organ movements, object occlusion, and (III) surgical scene variations due to different illuminations and viewpoints. Besides, action annotations in laparoscopy surgeries are limited and expensive due to requiring expert knowledge. In this study, we design and evaluate a CNN-RNN architecture as well as a customized training-inference framework to deal with the mentioned challenges in laparoscopic surgery action recognition. Using stacked recurrent layers, our proposed network takes advantage of inter-frame dependencies to negate the negative effect of content distortion and variation in action recognition. Furthermore, our proposed frame sampling strategy effectively manages the duration variations in surgical actions to enable action recognition with high temporal resolution. Our extensive experiments confirm the superiority of our proposed method in action recognition compared to static CNNs.