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UniMatch for Medical Image Segmentation

We provide the official PyTorch implementation of our UniMatch in the scenario of semi-supervised medical image segmentation:

Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
Lihe Yang, Lei Qi, Litong Feng, Wayne Zhang, Yinghuan Shi
In Conference on Computer Vision and Pattern Recognition (CVPR), 2023

Results

You can refer to our training logs for convenient comparisons during reproducing.

ACDC

Method 1 case 3 cases 7 cases
SupBaseline 28.5 41.5 62.5
UA-MT N/A 61.0 81.5
CPS N/A 60.3 83.3
CNN & Transformer N/A 65.6 86.4
UniMatch (Ours) 85.4 88.9 89.9

Getting Started

Installation

cd UniMatch
conda create -n unimatch python=3.10.4
conda activate unimatch
pip install -r requirements.txt
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html

Dataset

Please modify your dataset path in configuration files.

├── [Your ACDC Path]
    └── data

Usage

UniMatch

# use torch.distributed.launch
# switch to current folder
sh scripts/train.sh <num_gpu> <port>

To train on other datasets or splits, please modify dataset and split in train.sh.

Supervised Baseline

Modify the method from 'unimatch' to 'supervised' in train.sh, and double the batch_size in configuration file if you use the same number of GPUs as semi-supervised setting (no need to change lr).

Citation

If you find this project useful, please consider citing:

@inproceedings{unimatch,
  title={Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation},
  author={Yang, Lihe and Qi, Lei and Feng, Litong and Zhang, Wayne and Shi, Yinghuan},
  booktitle={CVPR},
  year={2023}
}

Acknowledgement

The processed ACDC dataset is borrowed from SSL4MIS.