| --- |
| license: mit |
| tags: |
| - medical |
| - radiology |
| - image-classification |
| - pytorch |
| - radimagenet |
| - feature-extraction |
| library_name: pytorch |
| --- |
| |
| # 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 |
|
|
| ```python |
| 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: |
|
|
| ```python |
| 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: |
|
|
| ```bibtex |
| @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 |
|
|
| Copyright (c) 2021 BMEII-AI |
|
|
| 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](https://github.com/BMEII-AI/RadImageNet) |
| - **Paper**: [RadImageNet: An Open Radiologic Deep Learning Research Dataset](https://pubs.rsna.org/doi/10.1148/ryai.210315) |
| - **Dataset**: The RadImageNet dataset contains 1.35 million annotated radiologic images |
|
|
