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
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}
-
For Training! run:
bash training_scripts/run_isic.shwhere the first command line is usedpython 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} -
For Testing! run:
bash training_scripts/run_isic.shwhere the second command line is usedpython 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}
}