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Semi-supervised Spatial Temporal Attention Network for Video Polyp Segmentation

Introduction

This repository contains the fully-supervised(fully-supervised for training, unsupervised for testing) version in SUN-SEG dataset of:

Semi-supervised Spatial Temporal Attention Network for Video Polyp Segmentation, MICCAI 2022 PDF

Although our work was based on semi-supervised learning, the fully-supervised version still achieves state-of-the-art results in most metrics.

Usage

This repository is based on GewelsJI/VPS, we strongly recommend you read their work first.

  • Preparing the SUN-SEG dataset

    Please refer to DATA_PREPARATION

  • Prerequisites of environment:

      pip install torch yacs einops timm tqdm tensorboardX opencv-python albumentations

    Because we don't use the NS block, it is not necessary to compile it.

  • Training:

    python ./scripts/my_train.py

  • Testing:

    Downloading pre-trained weights and move it into snapshot/SSTAN/epoch_15/SSTAN.pth, which can be found in this download link: MEGA

    python ./scripts/my_test.py

  • Evaluating:

     cd eval 
 bash eval.sh

Results

You can directly download the prediction maps of our approach in this download link: MEGA

  • Quantitative comparison on two testing sub-datasets, SUN-SEG-Easy (Unseen) and SUN-SEG-Hard (Unseen):

Existing Methods: source


Ours:

Dataset Method Smeasure meanEm wFmeasure meanFm maxDice meanSen
SUN-SEG-Easy(Unseen) 2022-MICCAI-SSTAN 0.805 0.838 0.691 0.745 0.726 0.662
SUN-SEG-Hard(Unseen) 2022-MICCAI-SSTAN 0.801 0.833 0.682 0.734 0.718 0.676

Citations

If you feel this work is helpful, please cite our paper

@inproceedings{zhao2022semi,
  title={Semi-supervised Spatial Temporal Attention Network for Video Polyp Segmentation},
  author={Zhao, Xinkai and Wu, Zhenhua and Tan, Shuangyi and Fan, De-Jun and Li, Zhen and Wan, Xiang and Li, Guanbin},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={456--466},
  year={2022},
  organization={Springer}
}

Changes

  • vacs.py & vit_utils.py
    • add SSTAN
  • dataloader.py & my_test.py
    • ensure that the frames input to the network are continuous
  • my_train.py
    • change the loss function
    • fix the logging
  • config.py

Acknowledgements

  • This codebase is based on GewelsJI/VPS. Thanks very much for their wonderful work!