RadImageNet Pre-trained Models

This repository contains pre-trained models from RadImageNet, a large-scale radiologic image dataset designed to facilitate transfer learning for medical imaging applications.

Model Description

RadImageNet models are convolutional neural networks pre-trained on a diverse collection of radiologic images spanning multiple modalities and anatomical regions. These models serve as powerful feature extractors for downstream medical imaging tasks.

Available Models

ResNet50.pt: ResNet-50 architecture pre-trained on RadImageNet
DenseNet121.pt: DenseNet-121 architecture pre-trained on RadImageNet
InceptionV3.pt: Inception-V3 architecture pre-trained on RadImageNet

Usage

import torch
from huggingface_hub import hf_hub_download

# Download and load a model
model_path = hf_hub_download(repo_id="Lab-Rasool/RadImageNet", filename="ResNet50.pt")
model = torch.load(model_path, map_location="cuda" if torch.cuda.is_available() else "cpu")
model.eval()

# Use for inference
# ... your inference code here ...

Preprocessing

Images should be preprocessed using standard ImageNet normalization:

from torchvision import transforms

preprocess = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

Citation

If you use these models in your research, please cite the RadImageNet paper:

@article{mei2022radimagenet,
  title={RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning},
  author={Mei, Xueyan and Liu, Zelong and Robson, Philip M and Marinelli, Brett and Huang, Mingqian and Doshi, Amish and Jacobi, Adam and Cao, Chendi and Link, Katherine E and Yang, Thomas and others},
  journal={Radiology: Artificial Intelligence},
  volume={4},
  number={5},
  pages={e210315},
  year={2022},
  publisher={Radiological Society of North America}
}

License

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Additional Information

Original Repository: BMEII-AI/RadImageNet
Paper: RadImageNet: An Open Radiologic Deep Learning Research Dataset
Dataset: The RadImageNet dataset contains 1.35 million annotated radiologic images