torchxrayvision 0.0.2

A small example package

torchxrayvision

A library for chest X-ray datasets and models. Including pre-trainined models.

This code is still under development

models

model = xrv.models.DenseNet(weights="nih")
model = xrv.models.DenseNet(weights="chex")
model = xrv.models.DenseNet(weights="minix_nb")
model = xrv.models.DenseNet(weights="minix_ch")
model = xrv.models.DenseNet(weights="all")

datasets

transform = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop(),
                                            xrv.datasets.XRayResizer(224)])

d_kaggle = xrv.datasets.Kaggle_Dataset(imgpath="path to stage_2_train_images_jpg",
                                       transform=transform)

d_chex = xrv.datasets.CheX_Dataset(imgpath="path to CheXpert-v1.0-small",
                                   csvpath="path to CheXpert-v1.0-small/train.csv",
                                   transform=transform)

d_nih = xrv.datasets.NIH_Dataset(imgpath="path to NIH images")

d_nih2 = xrv.datasets.NIH_Google_Dataset(imgpath="path to NIH images")

d_pc = xrv.datasets.PC_Dataset(imgpath="path to image folder")


d_covid19 = xrv.datasets.COVID19_Dataset()

dataset tools

relabel_dataset will align labels to have the same order as the pathologies argument.

xrv.datasets.relabel_dataset(pathologies, d_nih) # has side effects

Cite:

Joseph Paul Cohen, Joseph Viviano, and Hadrien Bertrand. TorchXrayVision: A library of chest X-ray datasets and models. https://github.com/mlmed/torchxrayvision, 2020

and

Cohen, J. P., Hashir, M., Brooks, R., & Bertrand, H. On the limits of cross-domain generalization in automated X-ray prediction. 2020 arXiv preprint arXiv:2002.02497.

@article{cohen2020limits,
  title={On the limits of cross-domain generalization in automated X-ray prediction},
  author={Cohen, Joseph Paul and Hashir, Mohammad and Brooks, Rupert and Bertrand, Hadrien},
  journal={arXiv preprint arXiv:2002.02497},
  year={2020}
}