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--- |
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license: apache-2.0 |
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tags: |
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- vision |
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- image-classification |
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datasets: |
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- nih-pc-chex-mimic_ch-google-openi-rsna |
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--- |
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# densenet121-res224-all |
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A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers. |
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This model was trained on the datasets: nih-pc-chex-mimic_ch-google-openi-rsna and is described here: https://arxiv.org/abs/2002.02497 |
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### How to use |
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Here is how to use this model to classify an image of xray: |
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```python |
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import urllib.request |
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import skimage |
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import torch |
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import torch.nn.functional as F |
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import torchvision |
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import torchvision.transforms |
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import torchxrayvision as xrv |
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model_name = "densenet121-res224-all" |
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img_url = "https://huggingface.co/spaces/torchxrayvision/torchxrayvision-classifier/resolve/main/16747_3_1.jpg" |
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img_path = "xray.jpg" |
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urllib.request.urlretrieve(img_url, img_path) |
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model = xrv.models.get_model(model_name, from_hf_hub=True) |
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img = skimage.io.imread(img_path) |
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img = xrv.datasets.normalize(img, 255) |
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# Check that images are 2D arrays |
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if len(img.shape) > 2: |
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img = img[:, :, 0] |
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if len(img.shape) < 2: |
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print("error, dimension lower than 2 for image") |
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# Add color channel |
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img = img[None, :, :] |
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transform = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop()]) |
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img = transform(img) |
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with torch.no_grad(): |
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img = torch.from_numpy(img).unsqueeze(0) |
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preds = model(img).cpu() |
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output = { |
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k: float(v) |
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for k, v in zip(xrv.datasets.default_pathologies, preds[0].detach().numpy()) |
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} |
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print(output) |
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``` |
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For more code examples, we refer to the [example scripts](https://github.com/kamalkraj/torchxrayvision/blob/master/scripts). |
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### Citation |
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Primary TorchXRayVision paper: [https://arxiv.org/abs/2111.00595](https://arxiv.org/abs/2111.00595) |
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``` |
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Joseph Paul Cohen, Joseph D. Viviano, Paul Bertin, Paul Morrison, Parsa Torabian, Matteo Guarrera, Matthew P Lungren, Akshay Chaudhari, Rupert Brooks, Mohammad Hashir, Hadrien Bertrand |
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TorchXRayVision: A library of chest X-ray datasets and models. |
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https://github.com/mlmed/torchxrayvision, 2020 |
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@article{Cohen2020xrv, |
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author = {Cohen, Joseph Paul and Viviano, Joseph D. and Bertin, Paul and Morrison, Paul and Torabian, Parsa and Guarrera, Matteo and Lungren, Matthew P and Chaudhari, Akshay and Brooks, Rupert and Hashir, Mohammad and Bertrand, Hadrien}, |
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journal = {https://github.com/mlmed/torchxrayvision}, |
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title = {{TorchXRayVision: A library of chest X-ray datasets and models}}, |
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url = {https://github.com/mlmed/torchxrayvision}, |
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year = {2020} |
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arxivId = {2111.00595}, |
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} |
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``` |
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and this paper which initiated development of the library: [https://arxiv.org/abs/2002.02497](https://arxiv.org/abs/2002.02497) |
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``` |
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Joseph Paul Cohen and Mohammad Hashir and Rupert Brooks and Hadrien Bertrand |
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On the limits of cross-domain generalization in automated X-ray prediction. |
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Medical Imaging with Deep Learning 2020 (Online: https://arxiv.org/abs/2002.02497) |
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@inproceedings{cohen2020limits, |
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title={On the limits of cross-domain generalization in automated X-ray prediction}, |
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author={Cohen, Joseph Paul and Hashir, Mohammad and Brooks, Rupert and Bertrand, Hadrien}, |
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booktitle={Medical Imaging with Deep Learning}, |
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year={2020}, |
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url={https://arxiv.org/abs/2002.02497} |
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} |
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``` |
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