This is the official implementation of the paper "CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization". The pre-print version can be found in arxiv; the early access version can be found in TMI.
data_preporcess/prep_mayo2016.py
) and its corresponding training demo (train_mayo2016.sh
).Please check train.sh
for training script (or test.sh
for inference script) once the data is well prepared. Specify the setting in the script, and simply run it in the terminal.
For one-shot learning framework,please check train_osl_framework_training.sh
for training script (or test_osl_framework.sh
for inference script)
These curves are calculated based on our simulated 5% dose data.
- Linux Platform
- python==3.8.13
- cuda==10.2
- torch==1.10.1
- torchvision=0.11.2
- numpy=1.23.1
- scipy==1.10.1
- h5py=3.7.0
- pydicom=2.3.1
- natsort=8.2.0
- scikit-image=0.21.0
- einops=0.4.1
- tqdm=4.64.1
- wandb=0.13.3
If you find our work and code helpful, please kindly cite the corresponding paper:
@article{gao2023corediff,
title={CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization},
author={Gao, Qi and Li, Zilong and Zhang, Junping and Zhang, Yi and Shan, Hongming},
journal={IEEE Transactions on Medical Imaging},
year={2023}
}