Official repository for "Classification of Multi-Parametric Body MRI Series Using Deep Learning", published in IEEE Journal of Biomedical and Health Informatics.
You may need Conda environment. You can download Anaconda from this link. Then, please run:
conda env create –f mriclassifier_env.yml
conda activate mriclassifier
This code runs using NIFTI (.nii.gz) files. Please put your NIFTI files into ./data. For preprocessing data, please run:
python data_preprocessing.py
Then, the processed data will be saved in ./data/preprocessed.
- If you use our pre-trained model, please preprocess the data using the above code, which resamples the 3D MRI volumes.
To train our model, run the following command:
sh train.sh
The checkpoints of the model will be saved in ./checkpoint/.
To test the trained our model, run:
sh test.sh
The results of the MRI classification such as the confusion matrix will be saved in ./results.
- Here, we have provided our pre-trained model which was trained on the Siemens scanner data and then finetuned on the Philips scanner data (Please see Strategy 2 in Table 2 of the paper).
- To use our pre-trained model, please download the model weights from here and put the weights in ./pretrained_model.
@article{kimclassification,
title={Classification of Multi-Parametric Body MRI Series Using Deep Learning},
author={Kim, Boah and Mathai, Tejas Sudharshan and Helm, Kimberly and Pinto, Peter A and Summers, Ronald M},
journal={IEEE journal of biomedical and health informatics}
}