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--- |
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title: Medgan |
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emoji: ⚡ |
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colorFrom: blue |
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colorTo: gray |
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sdk: static |
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pinned: false |
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license: mit |
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short_description: The project focuses on brain tumor MRI scans and includes im |
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--- |
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<<<<<<< HEAD |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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======= |
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[](https://github.com/mozaloom/medgan/actions/workflows/main.yml) |
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[](https://github.com/mozaloom/medgan/actions/workflows/push-docker.yml) |
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# MedGAN: Advanced Medical Image Generation |
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<img src="static/css/Blue_ABstract_Brain_Technology_Logo__1_-removebg-preview.png" alt="medgan Logo" width="120" style="margin-bottom: 20px;"> |
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## Overview |
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MedGAN is a comprehensive framework for generating high-quality synthetic medical images using state-of-the-art Generative Adversarial Networks (GANs). The project focuses on brain tumor MRI scans and includes implementations of multiple cutting-edge GAN architectures optimized for medical imaging applications. |
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## Features |
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- **Multiple GAN Implementations:** |
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- DCGAN (Deep Convolutional GAN) |
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- ProGAN (Progressive Growing of GANs) |
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- StyleGAN2 (Style-based Generator with improvements) |
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- WGAN (Wasserstein GAN with gradient penalty) |
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- **Web Application Interface:** |
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- Generate synthetic brain MRI scans |
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- Detect tumor types from uploaded MRI images |
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- Interactive and user-friendly interface |
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- **Pre-trained Models:** |
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- Models for three tumor types: Glioma, Meningioma, and Pituitary |
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- ViT-based tumor detection model (92% accuracy) |
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## Architecture Performance Comparison |
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| Architecture | Image Quality | Training Stability | Generation Diversity | Training Speed | |
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|--------------|---------------|--------------------|-----------------------|---------------| |
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| ProGAN | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | |
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| StyleGAN2 | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐ | |
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| WGAN-GP | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ | |
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| DCGAN | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐⭐ | |
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## Getting Started |
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### Prerequisites |
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- Python 3.9+ |
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- PyTorch 1.9+ |
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- Flask (for web application) |
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- CUDA-capable GPU (recommended) |
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### Installation |
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1. Clone the repository: |
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```bash |
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git clone https://github.com/mozaloom/medgan.git |
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cd medgan |
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``` |
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2. Install required packages: |
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```bash |
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pip install -r requirements.txt |
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``` |
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3. Run the web application: |
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```bash |
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python app.py |
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``` |
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4. Access the web interface at `http://localhost:5000` |
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## Usage |
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### Web Application |
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The MedGAN web application offers two primary functionalities: |
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1. **Generate synthetic brain MRI scans:** |
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- Select tumor type (Glioma, Meningioma, Pituitary) |
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- Choose GAN architecture |
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- Generate high-quality synthetic MRI images |
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2. **Detect tumor types:** |
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- Upload brain MRI scans |
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- Receive AI-powered tumor classification |
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- View detection confidence scores |
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Check the individual model implementation files for specific training parameters. |
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## Project Structure |
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``` |
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medgan/ |
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├── app.py # Flask web application |
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├── medgan/ # Core GAN implementations |
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│ ├── dcgan.py |
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│ ├── progan.py |
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│ ├── stylegan.py |
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│ ├── wgan.py |
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│ └── vit.py |
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├── models/ # Pre-trained model weights |
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├── notebooks/ # Training notebooks |
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│ ├── dcgan/ |
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│ ├── progan/ |
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│ ├── stylegan/ |
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│ └── wgan/ |
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├── static/ # Web assets |
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└── templates/ # HTML templates |
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``` |
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## Contributing |
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Contributions are welcome! Please feel free to submit a Pull Request. |
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1. Fork the repository |
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2. Create your feature branch (`git checkout -b feature/amazing-feature`) |
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3. Commit your changes (`git commit -m 'Add some amazing feature'`) |
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4. Push to the branch (`git push origin feature/amazing-feature`) |
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5. Open a Pull Request |
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## License |
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This project is licensed under the MIT License - see the LICENSE file for details. |
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## Acknowledgments |
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- [Brain Tumor MRI Dataset](https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset/data) from Kaggle |
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- Research papers implementing the original GAN architectures: |
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- [DCGAN](https://arxiv.org/abs/1511.06434) |
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- [ProGAN](https://arxiv.org/abs/1710.10196) |
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- [StyleGAN2](https://arxiv.org/abs/1912.04958) |
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- [WGAN](https://arxiv.org/abs/1701.07875) |
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>>>>>>> c38c95c (Initial commit) |
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