code for the paper: Context-aware Voxel-wise Contrastive Learning for Label Efficient Multi-organ Segmentation
python 3.7.8
pytorch 1.12.1
CUDA 10.2
python train_model.py --config_file train_config.yaml
python test_model.py --config_file test_config.yaml
Thanks Partially-supervised-multi-organ-segmentation and nnUNet for their wonderfurl work. Part of the code is borrowed from them. Please feel free to cite their work:
@article{shi2021marginal,
title={Marginal loss and exclusion loss for partially supervised multi-organ segmentation},
author={Shi, Gonglei and Xiao, Li and Chen, Yang and Zhou, S Kevin},
journal={Medical Image Analysis},
volume={70},
pages={101979},
year={2021},
publisher={Elsevier}
}
@article{isensee2021nnu,
title={nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
author={Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
journal={Nature methods},
volume={18},
number={2},
pages={203--211},
year={2021},
publisher={Nature Publishing Group US New York}
}