Biomedical Imaging with DDPM

Welcome to the Biomedical Imaging project using Denoising Diffusion Probabilistic Models (DDPM). This repository contains the code and resources needed to run and understand the DDPM model for biomedical imaging applications.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Contributing
• License

Introduction

Denoising Diffusion Probabilistic Models (DDPM) are a class of generative models that have shown great promise in generating high-quality images. This project focuses on applying DDPM to biomedical imaging, aiming to enhance image quality and provide better diagnostic tools.

Features

• High-Quality Image Generation: Generate high-resolution biomedical images.
• Noise Reduction: Effectively reduce noise in medical images.
• Customizable Parameters: Fine-tune the model parameters for specific imaging tasks.

Installation

To get started with this project, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/biomedical-imaging-ddpm.git
   cd biomedical-imaging-ddpm

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

To run the DDPM model on your biomedical images, follow these steps:

1. Prepare your dataset: Ensure your biomedical images are in the data/ directory.

2. Train the model:

   python train.py --config configs/default.yaml

3. Generate images:

   python generate.py --model checkpoints/model.pth --output results/

4. Evaluate the results: Check the generated images in the results/ directory.

Contributing

We welcome contributions to this project! If you have any ideas, suggestions, or bug reports, please open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

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Thank you for using our Biomedical Imaging with DDPM project. We hope it helps you achieve better imaging results!

Citation

If you use this code or find our work helpful, please cite our paper:

<!-- @article{yourname2024biomedical,
    title={Biomedical Imaging with Denoising Diffusion Probabilistic Models},
    author={Your Name and Collaborator Name},
    journal={Journal of Biomedical Imaging},
    year={2024},
    volume={10},
    pages={123-456},
    publisher={Biomedical Publishing}
}
``` -->

## Acknowledgements

We would like to thank the contributors and the open-source community for their valuable work and support. Special thanks to the developers of the libraries and tools that made this project possible.