DiffMIC is a project to adapt Diffusion Probabilistic Models to general medical image classification by dual-granularity conditional guidance. The method is elaborated in the paper DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification.

• 23-06-05. This paper has been early accepted by MICCAI 2023. Code is coming and welcome to taste it.
• 23-06-06. This project is still quickly updating 🌝. Check TODO list to see what will be released next.

conda env create -f environment.yml

1. Download HAM10000 or APTOS2019 dataset. Your dataset folder under "your_data_path" should be like:

dataset/isic2018/

 images/...
 
 ISIC2018_Task3_Training_GroundTruth.csv
 
 isic2018_train.pkl

 isic2018_test.pkl

dataset/aptos/

 train/...
 
 train.csv
 
 aptos_train.pkl

 aptos_test.pkl

.pkl file contains the list of data whose element is a dictionary with the format as {'img_root':image_path,'label':label}

2. For Training! run: bash training_scripts/run_isic.sh where the first command line is used python main.py --device ${DEVICE_ID} --thread ${N_THREADS} --loss ${LOSS} --config configs/${TASK}.yml --exp $EXP_DIR/${MODEL_VERSION_DIR} --doc ${TASK} --n_splits ${N_SPLITS}

3. For Testing! run: bash training_scripts/run_isic.sh where the second command line is used python main.py --device ${DEVICE_ID} --thread ${N_THREADS} --loss ${LOSS} --config $EXP_DIR/${MODEL_VERSION_DIR}/logs/ --exp $EXP_DIR/${MODEL_VERSION_DIR} --doc ${TASK} --n_splits ${N_SPLITS} --test --eval_best

The configuration for each of the above-listed tasks (including data file location, training log and evaluation result directory settings, neural network architecture, optimization hyperparameters, etc.) are provided in the corresponding files in the configs directory

• Release PMG2000 dataset and config
• Release HAM10000, APTOS2019 dataloaders and configs
• Dataset splits
• Release training scripts
• Release evaluation
• Upload the checkpoints of HAM10000, APTOS2019
• configuration

Welcome to contribute to DiffMIC. Any technique that can improve the performance or speed up the algorithm is appreciated🙏. I am writing DiffMIC V2, aiming at top journals. I'm glad to list the contributors as my co-authors🤗.

Code is largely based on XzwHan/CARD, CompVis/stable-diffusion, MedSegDiff, nyukat/GMIC

If you find this code useful, please cite

@article{yang2023diffmic,
  title={DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification},
  author={Yang, Yijun and Fu, Huazhu and Aviles-Rivero, Angelica and Sch{\"o}nlieb, Carola-Bibiane and Zhu, Lei},
  journal={arXiv preprint arXiv:2303.10610},
  year={2023}
}