Awesome
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.
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Preparing the SUN-SEG dataset
Please refer to
DATA_PREPARATION
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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.
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Training:
python ./scripts/my_train.py
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Testing:
Downloading pre-trained weights and move it into
snapshot/SSTAN/epoch_15/SSTAN.pth
, which can be found in this download link: MEGApython ./scripts/my_test.py
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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
<p align="center"> <img src="./assets/ModelPerformance.png"/> <br /> </p>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!