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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.
| Date | Stars |
|---|---|
| 2026-07-24 | 1176 |
| 2026-07-25 | 1176 |
| 2026-07-28 | 1176 |
| 2026-07-30 | 1176 |
| 2026-08-06 | 1176 |
Today
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Momentum
10.0
growth rate 0.00%/day
🚨 Paper now online! [https://arxiv.org/abs/2111.00595](https://arxiv.org/abs/2111.00595)
🚨 Documentation now online! [https://mlmed.org/torchxrayvision/](https://mlmed.org/torchxrayvision/)
# TorchXRayVision
| <img src="https://raw.githubusercontent.com/mlmed/torchxrayvision/main/docs/torchxrayvision-logo.png" width="300px"/> | ([🎬 promo video](https://www.youtube.com/watch?v=Rl7xz0uULGQ)) <br>[<img src="http://img.youtube.com/vi/Rl7xz0uULGQ/0.jpg" width="400px"/>)](http://www.youtube.com/watch?v=Rl7xz0uULGQ "Video Title") |
|---|---|
# What is it?
A library for chest X-ray datasets and models. Including pre-trained models.
TorchXRayVision is an open source software library for working with chest X-ray datasets and deep learning models. It provides a common interface and common pre-processing chain for a wide set of publicly available chest X-ray datasets. In addition, a number of classification and representation learning models with different architectures, trained on different data combinations, are available through the library to serve as baselines or feature extractors.
- In the case of researchers addressing clinical questions it is a waste of time for them to train models from scratch. To address this, TorchXRayVision provides pre-trained models which are trained on large cohorts of data and enables 1) rapid analysis of large datasets 2) feature reuse for few-shot learning.
- In the case of researchers developing algorithms it is important to robustly evaluate models using multiple external datasets. Metadata associated with each dataset can vary greatly which makes it difficult to apply methods to multiple datasets. TorchXRayVision provides access to many datasets in a uniform way so that they can be swapped out with a single line of code. These datasets can also be merged and filtered to construct specific distributional shifts for studying generalization.
Twitter: [@torchxrayvision](https://twitter.com/torchxrayvision)
## Getting started
```
$ pip install torchxrayvision
```
```python3
import torchxrayvision as xrv
import skimage, torch, torchvision
# Prepare the image:
img = skimage.io.imread("16747_3_1.jpg")
img = xrv.datasets.normalize(img, 255) # convert 8-bit image to [-1024, 1024] range
img = img.mean(2)[None, ...] # Make single color channel
transform = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop(),xrv.datasets.XRayResizer(224)])
img = transform(img)
img = torch.from_numpy(img)
# Load model and process image
model = xrv.models.DenseNet(weights="densenet121-res224-all")
outputs = model(img[None,...]) # or model.features(img[None,...])
# Print results
dict(zip(model.pathologies,outputs[0].detach().numpy()))
{'Atelectasis': 0.32797316,
'Consolidation': 0.42933336,
'Infiltration': 0.5316924,
'Pneumothorax': 0.28849724,
'Edema': 0.024142697,
'Emphysema': 0.5011832,
'Fibrosis': 0.51887786,
'Effusion': 0.27805611,
'Pneumonia': 0.18569896,
'Pleural_Thickening': 0.24489835,
'Cardiomegaly': 0.3645515,
'Nodule': 0.68982,
'Mass': 0.6392845,
'Hernia': 0.00993878,
'Lung Lesion': 0.011150705,
'Fracture': 0.51916164,
'Lung Opacity': 0.59073937,
'Enlarged Cardiomediastinum': 0.27218717}
```
A sample script to process images usings pretrained models is [process_image.py](https://github.com/mlmed/torchxrayvision/blob/main/scripts/process_image.py)
```
$ python3 process_image.py ../tests/00000001_000.png -resize
{'preds': {'Atelectasis': 0.50577986,
'Cardiomegaly': 0.62151504,
'Consolidation': 0.3124331,
'Edema': 0.21286564,
'Effusion': 0.39427388,
'Emphysema': 0.503361,
'Enlarged Cardiomediastinum': 0.4313866,
'Fibrosis': 0.5401596,
'Fracture': 0.28907478,
'Hernia': 0.012677962,
'Infiltration': 0.5220189,
'Lung Lesion': 0.21828467,
'Lung Opacity': 0.36826086,
'Mass': 0.4104132,
'Nodule': 0.5091791,
'Pleural_TExcerpt of 17,192 characters
Read on GitHub
Joseph Paul Cohen PhD
386
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Jean-Rémi KING · Meta AI, CNRS · France
2
2
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Abdolkarim Saeedi · Iran
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hari
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7be6e5ce6e0900c7, topic:deep-learning, topic:pytorch
matched fp:7be6e5ce6e0900c7, topic:foundation-models
matched fp:7be6e5ce6e0900c7, topic:dataset, readme:dataset, desc:datasets
matched fp:7be6e5ce6e0900c7, topic:medical, readme:clinical