TorchXrayVision: A library of chest X-ray datasets and models
Project description
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
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
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
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
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](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}
}
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