Multi-Domain Balanced Sampling

This repository is the official implementation of the paper Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models accepted at Medical Imaging meets NeurIPS 2021 (Med-NeurIPS 2021).
The arXiv version can be found here.

September 2022 Updates - Quantization Aware Training

We have added the script chest_quantize.py to allow users run the experiment on a CPU with a smaller model size. For now, only ResNet-50 is supported.
One can train a quantized model by running:

python chest_quantize.py --dataset_dir <data dir> --train_datas cx pc --val_data mc --seed 0

Test the model by running:

python chest_quantize.py --dataset_dir <data dir> --train_datas cx pc --val_data mc --seed 0 --test_only --test_data nih

Four (4) Pathologies, Four (4) Datasets, & Leave-One-out Cross-Validation

There are 12 different training, validation and test settings generated by combining 4 different Chest X-ray datasets (NIH ChestX-ray8 dataset, PadChest dataset, CheXpert, and MIMIC-CXR). The dataset names are condensed as short strings: "nih"= NIH ChestX-ray8 dataset, "pc" = PadChest dataset, "cx" = CheXpert, and "mc" = MIMIC-CXR.
The models used in this experiment are: DenseNet-121 and ResNet-50.
For each experiment, we compute the ROC-AUC for the following chest x-ray pathologies (labels): Cardiomegaly, Effusion, Edema, Consolidation.

For each experiment, train on two (2) datasets, validate on one (1) leave-out set and test on the remaining one (1) leave-out set.
The chest.py file contains code to run the models in this study.

Train Using Baseline Model (Merged Datasets)

To train a DenseNet-121 model with the Baseline approach, on CheXpert and PadChest, and validate on the MIMIC-CXR dataset, with seed=0 run the following code:

python chest.py --merge_train --arch densenet121 --train_datas cx pc --val_data mc --seed 0

Train Balanced Mini-Batch Sampling

To train a DenseNet-121 model with the Balanced Mini-Batch Sampling strategy on CheXpert and PadChest, and validate on the MIMIC-CXR dataset, with seed=0 run the following code:

python chest.py --arch densenet121 --train_datas cx pc --val_data mc --seed 0 

Inference

To run inference, add the arguments --test_only and test_data to the same code you pass for training.
Example: running inference on NIH dataset

python chest.py --arch densenet121 --train_datas cx pc --val_data mc --seed 0 --test_only --test_data nih

Inference using the XRV model

To perform inference using the DenseNet model with pretrained weights from torchxrayvision, run the following line of code:

python xrv_test.py --test_data pc --seed 0

Note that you can pass any of the arguments pc, mc, cx or nih to --test_data to run inference on PadChest, MIMIC-CXR, CheXpert and ChestX-Ray8 respectively.

Requirements (Installations)

In your terminal run pip install scikit-learn wandb torch torchvision torchxrayvision or

git clone https://github.com/etetteh/OoD_Gen-Chest_Xray.git
cd OoD_Gen-Chest_Xray
pip install -r requirements.txt

Citation

@inproceedings{tetteh2020balanced,
  title={Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models},
  author={Tetteh, Enoch and Viviano, Joseph and Bengio, Yoshua and Krueger, David and Cohen, Joseph Paul},
  booktitle={Medical Imaging Meets NeurIPS},
  publisher = {arXiv},
  year={2021},
  url={https://arxiv.org/abs/2112.13734}
}