Update README.md

README.md

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-# Refined-RadImageNet Model Card
+---
+language: en
+---
+
+# Refined RadImageNet - Conversion Tools
 
-## Model Description
+The RadImageNet dataset is available upon request at [https://www.radimagenet.com/](https://www.radimagenet.com/).
 
-**Refined-RadImageNet** is a collection of pre-trained convolutional neural networks (CNNs) specifically designed for medical imaging applications. These models are trained solely on the RadImageNet dataset, which comprises over 1.35 million annotated CT, MRI, and ultrasound images across various pathologies. 
+This repository provides tools to process the RadImageNet dataset, converting it into a refined and stratified organization suitable for various medical imaging applications.
 
-## Model Details
+For detailed information, refer to our preprint paper: [Policy Gradient-Driven Noise Mask](https://arxiv.org/abs/2406.14568).
 
-- **Developed by:** [ConvergingMachine](https://github.com/converging-machine)
-- **Model type:** Convolutional Neural Networks (ResNet variants)
-- **License:** Apache-2.0
-- **Finetuned from model:** RadImageNet
+If you use this code in your research, please cite our paper:
+
+```bibtex
+@article{yavuz2024policy,
+  title={Policy Gradient-Driven Noise Mask},
+  author={Yavuz, Mehmet Can and Yang, Yang},
+  year={2024},
+  eprint={2406.14568},
+  archivePrefix={arXiv},
+  primaryClass={eess.IV}
+}
+```
 
 ## Performance Comparison of ResNet Models
 
-This table compares the performance of ResNet models pretrained on 2D RadImageNet using regular and Two2Three convolution techniques across various metrics.
+This table compares the performance of ResNet models pretrained on 2D RadImageNet using regular and Two2Three convolution techniques across various metrics:
 
-| Model      | Precision (macro) | Recall (macro) | F1 Score (macro) | Balanced Accuracy | Average Accuracy |
-|------------|-------------------|----------------|------------------|-------------------|------------------|
-| ResNet10t  | 0.4720            | 0.3848         | 0.3998           | 0.3848            | 0.7981           |
-| ResNet18   | 0.5150            | 0.4383         | 0.4545           | 0.4383            | 0.8177           |
-| ResNet50   | 0.5563            | 0.4934         | 0.5097           | 0.4934            | 0.8352           |
+| Model     | Precision (macro) | Recall (macro) | F1 Score (macro) | Balanced Accuracy | Average Accuracy |
+|-----------|-------------------|----------------|------------------|-------------------|------------------|
+| ResNet10t | 0.4720            | 0.3848         | 0.3998           | 0.3848            | 0.7981           |
+| ResNet18  | 0.5150            | 0.4383         | 0.4545           | 0.4383            | 0.8177           |
+| ResNet50  | 0.5563            | 0.4934         | 0.5097           | 0.4934            | 0.8352           |
 
+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).
 
+## ResNet Models and Weights
 
-## Usage
+The model weights are shared through [https://huggingface.co/convergedmachine/RadImagenet](https://huggingface.co/convergedmachine/RadImagenet).
 
-To utilize the Refined-RadImageNet models in your project, follow these steps:
+To create a model using the `timm` library:
 
-1. **Install Git LFS (Large File Storage):**
+```python
+import timm
+model = timm.create_model('resnet10t', num_classes=165)
+```
 
-   ```bash
-   git lfs install
-   ```
+Replace `'resnet10t'` with `'resnet18'` or `'resnet50'` as needed.
 
-2. **Clone the Repository:**
+## Folder Structure
 
-   ```bash
-   git clone https://huggingface.co/ogrenenmakine/Refined-RadImagenet source/
-   ```
+```
+correction_masks/
+data/
+weights/
+output/
+source/
+    correction_masks.tar.gz
+    radimagenet.tar.gz
+    RadiologyAI_test.csv
+    RadiologyAI_train.csv
+    RadiologyAI_val.csv
+process.py
+measure_acc_metrics.py
+```
 
-   This command clones the repository into a directory named `source`. 
+### Files & Directories
 
-3. **Load the Model:**
+- **correction_masks/**: Contains correction masks for the images.
+- **data/**: Contains the extracted radiology images.
+- **weights/**: Directory for model weights.
+- **output/**: Directory for output files.
+- **source/**: Contains source files and datasets.
+  - **correction_masks.tar.gz**: Compressed file containing correction masks.
+  - **radimagenet.tar.gz**: Compressed RadImageNet dataset.
+  - **RadiologyAI_test.csv**: CSV file for the test dataset.
+  - **RadiologyAI_train.csv**: CSV file for the training dataset.
+  - **RadiologyAI_val.csv**: CSV file for the validation dataset.
+- **process.py**: Main script to process and organize the RadImageNet files.
+- **measure_acc_metrics.py**: Script to measure accuracy metrics.
 
-   The trained models are implemented using the `timm` library. Here's how to load a model:
+## Download Processing Files
 
-   ```python
-   import timm
-   model = timm.create_model('resnet10t', num_classes=165)
-   ```
+This repository contains files from the Hugging Face repository `convergedmachine/Refined-RadImagenet`. Follow the instructions below to clone the repository using Git.
 
-   Replace `'resnet10t'` with `'resnet18'` or `'resnet50'` as needed. 
+### Prerequisites
 
-## Dataset
+Ensure that Git LFS (Large File Storage) is installed:
 
-The RadImageNet dataset includes medical images of 3 modalities, 11 anatomies, and 165 pathologic labels. It is available upon request at [www.radimagenet.com](https://www.radimagenet.com/). 
+```bash
+git lfs install
+```
 
-## Citation
+### Cloning the Repository
 
-If you use this code or the Refined-RadImageNet models in your research, please cite the following paper:
+To clone the entire repository to your local machine:
 
-```bibtex
-@inproceedings{yavuz2025policy,
-  title={Policy Gradient-Driven Noise Mask},
-  author={Yavuz, Mehmet Can and Yang, Yang},
-  booktitle={International Conference on Pattern Recognition},
-  pages={414--431},
-  year={2025},
-  organization={Springer}
-}
+```bash
+git clone https://huggingface.co/convergedmachine/Refined-RadImagenet source/
 ```
 
-## License
+This command clones all files from the repository into a directory named `source`.
+
+### Notes
+
+- Ensure you have sufficient storage space for large files.
+- For more information about this dataset, visit the [Hugging Face page](https://huggingface.co/convergedmachine/Refined-RadImagenet).
+
+Feel free to contribute or raise issues if you encounter any problems.
+
+## Usage
+
+1. **Extract the Dataset**:
+
+   ```bash
+   python process.py
+   ```
+
+   Ensure the dataset tar file is located in the `source/` directory. The script will automatically extract it to the `data/` directory.
 
-This project is licensed under the Apache-2.0 License. 
+2. **Process the Images**:
 
-## Acknowledgements
+   The script will read the CSV files, refine the images, and organize them accordingly.
 
-Special thanks to the contributors of the [Refined-RadImageNet GitHub repository](https://github.com/converging-machine/Refined-RadImagenet) and the developers of the [RadImageNet dataset](https://github.com/BMEII-AI/RadImageNet).
+## Dependencies
+
+- Python 3.9+
+- pandas
+- OpenCV
+- tarfile
+- tqdm
+- numpy
+
+Install the required packages using pip:
+
+```bash
+pip install pandas opencv-python tarfile tqdm numpy
+```
 
 ---
 license: cc-by-2.0