metadata

language: en

Refined RadImageNet - Conversion Tools

The RadImageNet dataset is available upon request at https://www.radimagenet.com/.

This repository provides tools to process the RadImageNet dataset, converting it into a refined and stratified organization suitable for various medical imaging applications.

For detailed information, refer to our preprint paper: Policy Gradient-Driven Noise Mask.

If you use this code in your research, please cite our paper:

@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}
}

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:

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.

ResNet Models and Weights

The model codes are shared through https://github.com/convergedmachine/Refined-RadImagenet/.

To create a model using the timm library:

import timm
model = timm.create_model('resnet10t', num_classes=165)

Replace 'resnet10t' with 'resnet18' or 'resnet50' as needed.

Folder Structure

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

Files & Directories

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.

Download Processing Files

This repository contains files from the Hugging Face repository convergedmachine/Refined-RadImagenet. Follow the instructions below to clone the repository using Git.

Prerequisites

Ensure that Git LFS (Large File Storage) is installed:

git lfs install

Cloning the Repository

To clone the entire repository to your local machine:

git clone https://huggingface.co/convergedmachine/Refined-RadImagenet source/

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 Github page.

Feel free to contribute or raise issues if you encounter any problems.

Usage

Extract the Dataset:
```
python process.py
```
Ensure the dataset tar file is located in the source/ directory. The script will automatically extract it to the data/ directory.
Process the Images:

The script will read the CSV files, refine the images, and organize them accordingly.

Dependencies

Python 3.9+
pandas
OpenCV
tarfile
tqdm
numpy

Install the required packages using pip:

pip install pandas opencv-python tarfile tqdm numpy

LICENSE

This project is licensed under the MIT License.