3 D Med Diffusion LLM Model

Introduction

3D MedDiffusion is a framework designed for generating high-quality medical images across various modalities and organs. It employs diffusion models to create controllable and realistic 3D medical images. For further details, consult the project page or the academic paper.

Architecture

The architecture of 3D MedDiffusion leverages diffusion models to synthesize 3D medical images. This approach enables the generation of detailed and high-resolution images suitable for medical applications, facilitating the creation of images that span multiple medical imaging modalities.

Training

Information about the training process for 3D MedDiffusion is not explicitly detailed in the repository. However, diffusion models typically require substantial computational resources and large datasets to achieve high-quality image generation.

Guide: Running Locally

Clone the Repository:
Clone the 3D MedDiffusion repository to your local machine.
```
git clone https://huggingface.co/MMorss/3D-MedDiffusion
```
Set Up the Environment:
Set up a Python environment with the necessary dependencies. Typically, this involves installing PyTorch and any additional libraries specified in the repository.
```
pip install -r requirements.txt
```
Run the Model:
Execute the model scripts to generate 3D medical images. Specific commands and parameters may vary based on the implementation details.
Use Cloud GPUs:
For optimal performance, especially during training, consider using cloud GPU services such as AWS, Google Cloud, or Azure.

License

The licensing details for 3D MedDiffusion are not provided in the current documentation. It is recommended to refer to the repository or contact the authors for specific licensing terms.

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