EHRXDiff

Model card for our paper: Towards Predicting Temporal Changes in a Patient's Chest X-ray Images based on Electronic Health Records. We provide two versions of the EHRXDiff model:

• EHRXDiff – trained without the null-based augmentation technique
• EHRXDiff_{w_null} – trained with the null-based augmentation technique.

This card describes the EHRXDiff model. For implementation details, please refer to the EHRXDiff repository.

Installation

First, clone the repository and install the required packages:

git clone https://github.com/dek924/EHRXDiff.git

pip install "pip<24.1"
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
pip install -r requirements.txt

Loading the model

You can load the model directly in Python:

from cheff.ldm.models.diffusion.ddpm_tab import EHRXDiff

model = EHRXDiff.from_pretrained("dek924/ehrxdiff")
model.eval()

Alternatively, you can download the weights via the Hugging Face Hub:

from huggingface_hub import hf_hub_download

wt_path = hf_hub_download("dek924/ehrxdiff", "pytorch_model.bin")

and then run the evaluation script included in our github repository (scripts/eval.py):

python scripts/eval.py \
    --sdm_path=${CHECKPOINT_PATH}/pytorch_model.bin \
    --save_dir=${CHECKPOINT_PATH}/images/seed${RAND_SEED} \
    --img_meta_dir=${IMG_META_DIR} \   # Directory containing metadata for MIMIC-CXR-JPG
    --img_root_dir=${IMG_ROOT_DIR} \   # Directory containing preprocessed images
    --tab_root_dir=${TAB_ROOT_DIR} \   # Directory containing tabular data
    --seed=${RAND_SEED} \
    --batch_size=${BATCHSIZE}