metadata
license: mit
language:
- en
base_model:
- stable-diffusion-v1-5/stable-diffusion-v1-5
pipeline_tag: image-to-image
tags:
- medical
- CT
- autoencoders
MedVAE
Variational Autoencoders (VAEs) are widely used in imaging tasks such as image generation, reconstruction, and representation learning. However, most open-source VAEs are trained on natural images and are not suitable for 3D medical CT data. This creates a gap for researchers who need reliable VAE models that can handle real clinical CT volumes.
To address this problem, we benchmark MedVAE, a VAE pre-trained on large-scale 3D medical CT data. The goal is to evaluate whether MedVAE can better preserve anatomical structures and intensity distributions compared to general-purpose VAEs.
Benchmarks
| Name | VAE Type | # Patients | LPIPS | SSIM | PSNR | DSC Abdominal |
|---|---|---|---|---|---|---|
| stable-diffusion-v1-5 | KL-VAE | 0 | ||||
| stable-diffusion-3.5-large | KL-VAE | 0 | ||||
| 2D-MedVAE_KL-smooth-tianyu-2K | KL-VAE | 2k | ||||
| 2D-MedVAE_KL-smooth-kuma-10K | KL-VAE | 10k | ||||
| 2D-MedVAE_KL-sharp-kuma-10K | KL-VAE | 10k | ||||
| 2D-MedVAE_KL-sharp-kuma-20K | KL-VAE | 20k | ||||
| 2D-MedVAE_KL-sharp-kuma-100K | KL-VAE | 100k | ||||
| 2D-MedVAE_KL-sharp-kuma-300K | KL-VAE | 300k | ||||
| 3D-MedVAE_MAISI | KL-VAE | 40K | ||||
| 3D-MedVAE_KL-sharp-kuma-100K (ongoing) | KL-VAE | 100K | ||||
| 3D-MedVAE_KL-sharp-kuma-1M (ongoing) | KL-VAE | 1M | ||||
| 3D-MedVAE_VQ | VQ-VAE | 100K | ||||
| 3D-MedVAE_VQ-kuma-100K (ongoing) | VQ-VAE | 100K |
Citation
@article{liu2025see,
title={See More, Change Less: Anatomy-Aware Diffusion for Contrast Enhancement},
author={Liu, Junqi and Wu, Zejun and Bassi, Pedro RAS and Zhou, Xinze and Li, Wenxuan and Hamamci, Ibrahim E and Er, Sezgin and Lin, Tianyu and Luo, Yi and Płotka, Szymon and others},
journal={arXiv preprint arXiv:https://www.arxiv.org/abs/2512.07251},
year={2025},
url={https://github.com/MrGiovanni/SMILE}
}