Densenet121 Res224 Rsna

🚀 DenseNet121-Res224-RSNA

DenseNet121-Res224-RSNA是一种用于X光图像分类的卷积神经网络模型。DenseNet通过密集块（Dense Blocks）在各层之间建立密集连接，将所有特征图尺寸匹配的层直接相连。为保持前馈特性，每一层会接收前面所有层的输出作为额外输入，并将自己的特征图传递给后续所有层。

🚀 快速开始

模型使用说明

每个预训练模型有18个输出。all模型的所有输出都经过训练，但对于其他权重，部分目标未经过训练，会因训练数据集中不存在这些目标而随机预测。唯一有效的输出列在对应权重数据集的{dataset}.pathologies字段中。

模型的基准测试信息请参考：BENCHMARKS.md

代码示例

以下是使用该模型对X光图像进行分类的示例代码：

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-rsna"

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)

更多代码示例请参考：example scripts

📄 许可证

本项目采用Apache-2.0许可证。

📚 引用信息

主要的TorchXRayVision论文

• 论文链接：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},
}

发起该库开发的论文

• 论文链接：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}
}

📦 模型信息