The codes for Semi-supervised CT Lesion Segmentation Using Uncertainty-based Data Pairing and Swapmix
Implementation:
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Use UDiCT_env.yaml to configure your environment.
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Download LIDC-IDRI or COVID-SemiSeg dataset and unzip them. The LIDC-IDRI dataset used here is a manually processed version, which can be download at https://pantheon.corp.google.com/storage/browser/hpunet-data/lidc_crops from https://github.com/SimonKohl/probabilistic_unet.
The Covid-SemiSeg dataset can be download at https://drive.google.com/open?id=1bbKAqUuk7Y1q3xsDSwP07oOXN_GL3SQM from https://github.com/DengPingFan/Inf-Net.
The Seg-C19 dataset is collected by us privately, please contact us if you need it.
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Add the data folder path to dataload_inf.py, dataload_segc19.py and dataload_lidc.py
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run the .sh file in LIDC, SegC19 and COVIDSemiSeg to train your model.
Please consider citing this project in your publications if it helps your research.
@article{qiao2022semi,
title={Semi-Supervised CT Lesion Segmentation Using Uncertainty-Based Data Pairing and SwapMix},
author={Qiao, Pengchong and Li, Han and Song, Guoli and Han, Hu and Gao, Zhiqiang and Tian, Yonghong and Liang, Yongsheng and Li, Xi and Zhou, S Kevin and Chen, Jie},
journal={IEEE Transactions on Medical Imaging},
year={2022},
publisher={IEEE}
}