torchxrayvision 0.0.17

TorchXRayVision: A library of chest X-ray datasets and models

torchxrayvision

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

This code is still under development

Getting started

pip install torchxrayvision

import torchxrayvision as xrv

These are default pathologies:

xrv.datasets.default_pathologies 

['Atelectasis',
 'Consolidation',
 'Infiltration',
 'Pneumothorax',
 'Edema',
 'Emphysema',
 'Fibrosis',
 'Effusion',
 'Pneumonia',
 'Pleural_Thickening',
 'Cardiomegaly',
 'Nodule',
 'Mass',
 'Hernia',
 'Lung Lesion',
 'Fracture',
 'Lung Opacity',
 'Enlarged Cardiomediastinum']

models (demo notebook)

Specify weights for pretrained models (currently all DenseNet121) Note: Each pretrained model has 18 outputs. The all model has every output trained. However, for the other weights some targets are not trained and will predict randomly becuase they do not exist in the training dataset. The only valid outputs are listed in the field {dataset}.pathologies on the dataset that corresponds to the weights.

model = xrv.models.DenseNet(weights="all")
model = xrv.models.DenseNet(weights="kaggle") # RSNA Pneumonia Challenge
model = xrv.models.DenseNet(weights="nih") # NIH chest X-ray8
model = xrv.models.DenseNet(weights="pc") # PadChest (University of Alicante)
model = xrv.models.DenseNet(weights="chex") # CheXpert (Stanford)
model = xrv.models.DenseNet(weights="minix_nb") # MIMIC-CXR (MIT)
model = xrv.models.DenseNet(weights="minix_ch") # MIMIC-CXR (MIT)

datasets (demo notebook)

Only stats for PA/AP views are shown. Datasets may include more.

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

d_kaggle = xrv.datasets.RSNA_Pneumonia_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() # specify imgpath and csvpath for the dataset

National Library of Medicine Tuberculosis Datasets paper

d_nlmtb = xrv.datasets.NLMTB_Dataset(imgpath="path to MontgomerySet or ChinaSet_AllFiles")

Using MontgomerySet data:
NLMTB_Dataset num_samples=138 views=['PA']
{'Tuberculosis': {0: 80, 1: 58}}
or using ChinaSet_AllFiles data:
NLMTB_Dataset num_samples=662 views=['PA', 'AP']
{'Tuberculosis': {0: 326, 1: 336}}

dataset tools

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

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

specify a subset of views (demo notebook)

d_kaggle = xrv.datasets.RSNA_Pneumonia_Dataset(imgpath="...",
                                               views=["PA","AP","AP Supine"])

specify only 1 image per patient

d_kaggle = xrv.datasets.RSNA_Pneumonia_Dataset(imgpath="...",
                                               unique_patients=True)

obtain summary statistics per dataset

d_chex = xrv.datasets.CheX_Dataset(imgpath="CheXpert-v1.0-small",
                                   csvpath="CheXpert-v1.0-small/train.csv",
                                 views=["PA","AP"], unique_patients=False)

CheX_Dataset num_samples=191010 views=['PA', 'AP']
{'Atelectasis': {0.0: 17621, 1.0: 29718},
 'Cardiomegaly': {0.0: 22645, 1.0: 23384},
 'Consolidation': {0.0: 30463, 1.0: 12982},
 'Edema': {0.0: 29449, 1.0: 49674},
 'Effusion': {0.0: 34376, 1.0: 76894},
 'Enlarged Cardiomediastinum': {0.0: 26527, 1.0: 9186},
 'Fracture': {0.0: 18111, 1.0: 7434},
 'Lung Lesion': {0.0: 17523, 1.0: 7040},
 'Lung Opacity': {0.0: 20165, 1.0: 94207},
 'Pleural Other': {0.0: 17166, 1.0: 2503},
 'Pneumonia': {0.0: 18105, 1.0: 4674},
 'Pneumothorax': {0.0: 54165, 1.0: 17693},
 'Support Devices': {0.0: 21757, 1.0: 99747}}

Pathology masks (demo notebook)

d_rsna = xrv.datasets.RSNA_Pneumonia_Dataset(imgpath="stage_2_train_images_jpg", 
                                            views=["PA","AP"],
                                            pathology_masks=True)
d_rsna.csv.has_masks.value_counts()
False    20672
True      6012       

sample["pathology_masks"]

it also works with data_augmentation if you pass in data_aug=data_transforms to the dataloader. The random seed is matched to align calls for the image and the mask.

Citation

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

and this paper https://arxiv.org/abs/2002.02497

Cohen, J. P., Hashir, M., Brooks, R., & Bertrand, H. 
On the limits of cross-domain generalization in automated X-ray prediction. 
Medical Imaging with Deep Learning 2020 (Online: https://arxiv.org/abs/2002.02497)

@inproceedings{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},
  booktitle={Medical Imaging with Deep Learning}
  year={2020},
  url={https://arxiv.org/abs/2002.02497}
}