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language: en |
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# Refined RadImageNet - Conversion Tools |
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The RadImageNet dataset is available upon request at [https://www.radimagenet.com/](https://www.radimagenet.com/). |
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This repository provides tools to process the RadImageNet dataset, converting it into a refined and stratified organization suitable for various medical imaging applications. |
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For detailed information, refer to our preprint paper: [Policy Gradient-Driven Noise Mask](https://arxiv.org/abs/2406.14568). |
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If you use this code in your research, please cite our paper: |
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```bibtex |
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@article{yavuz2024policy, |
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title={Policy Gradient-Driven Noise Mask}, |
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author={Yavuz, Mehmet Can and Yang, Yang}, |
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year={2024}, |
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eprint={2406.14568}, |
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archivePrefix={arXiv}, |
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primaryClass={eess.IV} |
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} |
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``` |
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## Performance Comparison of ResNet Models |
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This table compares the performance of ResNet models pretrained on 2D RadImageNet using regular and Two2Three convolution techniques across various metrics: |
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| Model | Precision (macro) | Recall (macro) | F1 Score (macro) | Balanced Accuracy | Average Accuracy | |
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|-----------|-------------------|----------------|------------------|-------------------|------------------| |
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| ResNet10t | 0.4720 | 0.3848 | 0.3998 | 0.3848 | 0.7981 | |
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| ResNet18 | 0.5150 | 0.4383 | 0.4545 | 0.4383 | 0.8177 | |
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| ResNet50 | 0.5563 | 0.4934 | 0.5097 | 0.4934 | 0.8352 | |
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We recommend adapting the code for benchmarking other models, which can be found here: [https://github.com/pytorch/vision/tree/main/references/classification](https://github.com/pytorch/vision/tree/main/references/classification). |
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## ResNet Models and Weights |
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The model weights are shared through [https://huggingface.co/convergedmachine/RadImagenet](https://huggingface.co/convergedmachine/RadImagenet). |
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To create a model using the `timm` library: |
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```python |
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import timm |
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model = timm.create_model('resnet10t', num_classes=165) |
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``` |
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Replace `'resnet10t'` with `'resnet18'` or `'resnet50'` as needed. |
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## Folder Structure |
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``` |
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correction_masks/ |
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data/ |
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weights/ |
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output/ |
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source/ |
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correction_masks.tar.gz |
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radimagenet.tar.gz |
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RadiologyAI_test.csv |
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RadiologyAI_train.csv |
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RadiologyAI_val.csv |
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process.py |
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measure_acc_metrics.py |
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``` |
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### Files & Directories |
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- **correction_masks/**: Contains correction masks for the images. |
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- **data/**: Contains the extracted radiology images. |
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- **weights/**: Directory for model weights. |
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- **output/**: Directory for output files. |
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- **source/**: Contains source files and datasets. |
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- **correction_masks.tar.gz**: Compressed file containing correction masks. |
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- **radimagenet.tar.gz**: Compressed RadImageNet dataset. |
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- **RadiologyAI_test.csv**: CSV file for the test dataset. |
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- **RadiologyAI_train.csv**: CSV file for the training dataset. |
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- **RadiologyAI_val.csv**: CSV file for the validation dataset. |
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- **process.py**: Main script to process and organize the RadImageNet files. |
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- **measure_acc_metrics.py**: Script to measure accuracy metrics. |
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## Download Processing Files |
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This repository contains files from the Hugging Face repository `convergedmachine/Refined-RadImagenet`. Follow the instructions below to clone the repository using Git. |
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### Prerequisites |
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Ensure that Git LFS (Large File Storage) is installed: |
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```bash |
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git lfs install |
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``` |
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### Cloning the Repository |
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To clone the entire repository to your local machine: |
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```bash |
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git clone https://huggingface.co/convergedmachine/Refined-RadImagenet source/ |
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``` |
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This command clones all files from the repository into a directory named `source`. |
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### Notes |
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- Ensure you have sufficient storage space for large files. |
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- For more information about this dataset, visit the [Hugging Face page](https://huggingface.co/convergedmachine/Refined-RadImagenet). |
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Feel free to contribute or raise issues if you encounter any problems. |
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## Usage |
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1. **Extract the Dataset**: |
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```bash |
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python process.py |
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``` |
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Ensure the dataset tar file is located in the `source/` directory. The script will automatically extract it to the `data/` directory. |
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2. **Process the Images**: |
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The script will read the CSV files, refine the images, and organize them accordingly. |
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## Dependencies |
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- Python 3.9+ |
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- pandas |
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- OpenCV |
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- tarfile |
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- tqdm |
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- numpy |
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Install the required packages using pip: |
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```bash |
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pip install pandas opencv-python tarfile tqdm numpy |
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``` |
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license: cc-by-2.0 |
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