S2MTS2

This repo contains the Pytorch implementation of our paper:

Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification

Fengbei Liu*, Yu Tian*, Filipe R. Cordeiro, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro.

• Accepted at MICCAI MLMI2021 Workshop.

Requirements

• Linux
• Python 3.8
• Pytorch 1.6
• Pretrain with 4 * V100 and finetune with 1 * V100

Prepare Dataset

Download Chest Xray14 from https://nihcc.app.box.com/v/ChestXray-NIHCC/folder/36938765345

Pre-training and Fine-tuning

Prepare the dataset and then run the following command for pretrain:

python pretrain.py --data <data_dir> --multiprocessing-distributed --world-size 1 --rank 0 --batch-size 256 --lr 0.03 --arch densenet121 --mlp --cos --task chestxray14 --dist-url tcp://localhost:10001 --jcl

For Fine-tuning, run

python finetune.py --pretrained <pretrain model dir>

Citation

If you find this repo useful for your research, please consider citing our paper:

@article{liu2021self,
  title={Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification},
  author={Liu, Fengbei and Tian, Yu and Cordeiro, Filipe R and Belagiannis, Vasileios and Reid, Ian and Carneiro, Gustavo},
  journal={arXiv preprint arXiv:2103.03629},
  year={2021}
}

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