UDSM

This repository contains the code and newly-formulated datasets of our Unsupervised Feature Disentanglement of Scene and Motion (UDSM) model for video retrieval in minimally invasive surgery.

Requirements

• python >= 3.5
• pytroch >= 1.2.0
• cv2
• scipy

Usage

The datasets adopted is the Cholec80 dataset with prepocessing to facilitate the learning process and validation.

Training and testing

To train and test the UDSM model, first train the feature disentanglement module with train_disentangle.py , and then train the LSTM module with train_lstm.py.

The evaluation results are saved in a new directory, containig the files of Hamming distance, Hamming rank list and the mAP results.

Dataset

The retrieval dataset is formulated based on the public Cholec80 dataset.

• Download Cholec80 surgical video dataset
• Convert videos to frames and downsample from 25 fps to 1fps
• Cut the original video data into shorter clips, each accounting for 20 seconds (i.e., 20 frames)
• Discard tail frames in each phase to avoid phase transitions

Citation

If you find the code useful for your research, please cite:

@article{wang2022unsupervised,
  title={Unsupervised feature disentanglement for video retrieval in minimally invasive surgery},
  author={Wang, Ziyi and Lu, Bo and Gao, Xiaojie and Jin, Yueming and Wang, Zerui and Cheung, Tak Hong and Heng, Pheng Ann and Dou, Qi and Liu, Yunhui},
  journal={Medical Image Analysis},
  volume={75},
  pages={102296},
  year={2022},
  publisher={Elsevier}
}

Questions

For further question about the code or paper, please contact 'ziyiwangx@gmail.com'