Skip to content

Latest commit

 

History

History

eval

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
eval-result
eval-result
 
 
README.md
README.md
 
 
eval.sh
eval.sh
 
 
metrics.py
metrics.py
 
 
vps_evaluator.py
vps_evaluator.py
 
 

README.md

VPS Evaluation Toolbox

This toolbox is used to evaluate the performance of video polyp segmentation task.

Usage

  • Prerequisites of environment:

     python -m pip install opencv-python tdqm prettytable scikit-learn

  • Running the evaluation:

    sh ./eval.sh

    By running the script, results of all models on SUN-SEG dataset will be evaluated simultaneously. If you want to evaluate the specific models, please modify the $MODEL_NAMESvariable in eval.sh which is corresponding to the argument --model_lst. Note that the modified model name should be the same to the folder name under ./data/Pred/. In vps_evaluator.py, you can specify --metric_list to decide the applying metrics. --txt_name denotes the folder name of evaluation result. --data_lst and --check_integrity represent the used dataset and the integrity examination of result maps and ground truth.

Citation

If you have found our work useful, please use the following reference to cite this project:

@article{ji2022video,
  title={Video polyp segmentation: A deep learning perspective},
  author={Ji, Ge-Peng and Xiao, Guobao and Chou, Yu-Cheng and Fan, Deng-Ping and Zhao, Kai and Chen, Geng and Van Gool, Luc},
  journal={Machine Intelligence Research},
  volume={19},
  number={6},
  pages={531--549},
  year={2022},
  publisher={Springer}
}


@inproceedings{ji2021progressively,
  title={Progressively normalized self-attention network for video polyp segmentation},
  author={Ji, Ge-Peng and Chou, Yu-Cheng and Fan, Deng-Ping and Chen, Geng and Fu, Huazhu and Jha, Debesh and Shao, Ling},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={142--152},
  year={2021},
  organization={Springer}
}

@inproceedings{fan2020pranet,
  title={Pranet: Parallel reverse attention network for polyp segmentation},
  author={Fan, Deng-Ping and Ji, Ge-Peng and Zhou, Tao and Chen, Geng and Fu, Huazhu and Shen, Jianbing and Shao, Ling},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={263--273},
  year={2020},
  organization={Springer}
}