---
license: mit
---

# Med-VAE

Med-VAE is a family of six large-scale, generalizable 2D and 3D variational autoencoders (VAEs) designed for medical imaging. It is trained on over one million medical images across multiple anatomical regions and modalities. Med-VAE autoencoders encode medical images as downsized latent representations and decode latent representations back to high-resolution images. Across diverse tasks obtained from 20 medical image datasets, we demonstrate that utilizing MedVAE latent representations in place of high-resolution images when training downstream models can lead to efficiency benefits (up to 70x improvement in throughput) while simultaneously preserving clinically-relevant features. 

[💻 Github](https://github.com/StanfordMIMI/MedVAE)

## Model Description
| Total Compression Factor | Channels | Dimensions | Modalities | Anatomies | Config File | Model File |
|----------|----------|----------|----------|----------|----------|----------|
| 16 | 1 | 2D | X-ray | Chest, Breast (FFDM) | [medvae_4x1.yaml ](model_weights/medvae_4x1.yaml)| [vae_4x_1c_2D.ckpt](model_weights/vae_4x_1c_2D.ckpt)
| 16 | 3 | 2D | X-ray | Chest, Breast (FFDM) | [medvae_4x3.yaml](model_weights/medvae_4x3.yaml) | [vae_4x_3c_2D.ckpt](model_weights/vae_4x_3c_2D.ckpt)
| 64 | 1 | 2D | X-ray | Chest, Breast (FFDM) | [medvae_8x1.yaml](model_weights/medvae_8x1.yaml) | [vae_8x_1c_2D.ckpt](model_weights/vae_8x_1c_2D.ckpt)
| 64 | 3 | 2D | X-ray | Chest, Breast (FFDM) | [medvae_8x4.yaml](model_weights/medvae_8x4.yaml) | [vae_8x_4c_2D.ckpt](model_weights/vae_8x_4c_2D.ckpt)
| 64 | 1 | 3D | MRI, CT | Whole-Body | [medvae_4x1.yaml ](model_weights/medvae_4x1.yaml) | [vae_4x_1c_3D.ckpt](model_weights/vae_4x_1c_3D.ckpt)
| 512 | 1 | 3D | MRI, CT | Whole-Body | [medvae_8x1.yaml](model_weights/medvae_8x1.yaml) | [vae_8x_1c_3D.ckpt](model_weights/vae_8x_1c_3D.ckpt)

Note: Model weights and checkpoints are located in the `model_weights` folder.

## Usage Instructions



## Citation
If you use Med-VAE, please cite the original paper:

```bibtex
@article{varma2025medvae,
  title = {Med-VAE: --},
  author = {Maya Varma, Ashwin Kumar, Rogier van der Sluijs, Sophie Ostmeier, Louis Blankemeier, Pierre Chambon, Christian Bluethgen, Jip Prince, Curtis Langlotz, Akshay Chaudhari},
  year = {2025},
  publisher = {Arxiv},
  journal = {Arvix},
  howpublished = {TODO}
}
```

For questions, please place a Github Issues message.