model card

README.md

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+
+---
+license: apache-2.0
+tags:
+- vision
+- image-classification
+datasets:
+- nih-pc-chex-mimic_ch-google-openi-rsna
+---
+    
+    
+# densenet121-res224-all
+
+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.
+        
+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
+
+### How to use
+
+Here is how to use this model to classify an image of xray:
+
+```python
+import urllib.request
+
+import skimage
+import torch
+import torch.nn.functional as F
+import torchvision
+import torchvision.transforms
+
+import torchxrayvision as xrv
+
+model_name = "densenet121-res224-all"
+
+img_url = "https://huggingface.co/spaces/torchxrayvision/torchxrayvision-classifier/resolve/main/16747_3_1.jpg"
+img_path = "xray.jpg"
+urllib.request.urlretrieve(img_url, img_path)
+
+model = xrv.models.get_model(model_name, from_hf_hub=True)
+
+img = skimage.io.imread(img_path)
+img = xrv.datasets.normalize(img, 255)
+
+# Check that images are 2D arrays
+if len(img.shape) > 2:
+    img = img[:, :, 0]
+if len(img.shape) < 2:
+    print("error, dimension lower than 2 for image")
+
+# Add color channel
+img = img[None, :, :]
+
+transform = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop()])
+
+img = transform(img)
+
+with torch.no_grad():
+    img = torch.from_numpy(img).unsqueeze(0)
+    preds = model(img).cpu()
+    output = {
+        k: float(v)
+        for k, v in zip(xrv.datasets.default_pathologies, preds[0].detach().numpy())
+    }
+print(output)
+
+```
+For more code examples, we refer to the [example scripts](https://github.com/kamalkraj/torchxrayvision/blob/master/scripts).
+
+
+### Citation
+
+Primary TorchXRayVision paper: [https://arxiv.org/abs/2111.00595](https://arxiv.org/abs/2111.00595)
+
+```
+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
+TorchXRayVision: A library of chest X-ray datasets and models. 
+https://github.com/mlmed/torchxrayvision, 2020
+
+
+@article{Cohen2020xrv,
+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},
+journal = {https://github.com/mlmed/torchxrayvision},
+title = {{TorchXRayVision: A library of chest X-ray datasets and models}},
+url = {https://github.com/mlmed/torchxrayvision},
+year = {2020}
+arxivId = {2111.00595},
+}
+
+
+```
+and this paper which initiated development of the library: [https://arxiv.org/abs/2002.02497](https://arxiv.org/abs/2002.02497)
+```
+Joseph Paul Cohen and Mohammad Hashir and Rupert Brooks and Hadrien Bertrand
+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}
+}
+```
+