OoD_Gen-Chest_Xray

Out-of-Distribution Generalization of Chest X-ray Using Risk Extrapolation

Requirements (Installations)

Install the following libraries/packages with pip

torch 
torchvision
torchxrayvsion

Four (4) Pathologies, Four (4) Datasets, & 12-Fold 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). These 12 settings are broken down into 6 splits (ranging from 0 to 5) that can be called by passing the argument --split=<split>. For each split, you have the option to choose between 2 validation datasets by passing the argument --valid_data=<name of valid dataset>. The dataset names are condensed as short strings: "nih"= NIH ChestX-ray8 dataset, "pc" = PadChest dataset, "cx" = CheXpert, and "mc" = MIMIC-CXR.
For each setting, we compute the ROC-AUC for the following chest x-ray pathologies (labels): Cardiomegaly, Pneumonia, Effusion, Edema, Atelectasis, Consolidation, and Pneumothorax.

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

To finetune or perform feature extraction with ImageNet weights pass the --pretrained and --feat_extract arguments respectively

Train Using Baseline Model (Merged Datasets)

To train a DenseNet-121 Baseline model by fine-tuning on the first split, and validate on the MIMIC-CXR dataset, with seed=0 run the following code:

python chest.py --merge_train --arch densenet121 --pretrained --weight_decay=0.0 --split 0 --valid_data mc --seed 0

Note that for the first split, PadChest is automatically selected as the test_data, when you pass MIMIC-CXR as the validation data, and vice versa.

Train Balanced Mini-Batch Sampling

To train a DenseNet-121 Balanced Mini-Batch Sampling model by fine-tuning on the first split, and validate on the MIMIC-CXR dataset, with seed=0 run the following code:

python chest.py --arch densenet121 --pretrained --weight_decay=0.0 --split 0 --valid_data mc --seed 0

and always pass --weight_decay=0.0

If no model architecture is specified, the code trains all the following architectures: resnet50, and densenet121.

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 --dataset_name pc --seed 0

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