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--- |
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license: mit |
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language: |
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- en |
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base_model: |
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- stable-diffusion-v1-5/stable-diffusion-v1-5 |
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pipeline_tag: image-to-image |
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tags: |
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- medical |
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- CT |
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- autoencoders |
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--- |
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# MedVAE |
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*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. |
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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. |
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# Benchmarks |
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| Name | VAE Type | # Patients | LPIPS | SSIM | PSNR | DSC Abdominal | |
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|------------------|----------|---------------|--------|--------|--------|------------| |
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| stable-diffusion-v1-5 | KL-VAE | 0 | | | | | |
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| stable-diffusion-3.5-large | KL-VAE | 0 | | | | | |
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| 2D-MedVAE_KL-smooth-tianyu-2K | KL-VAE | 2k | | | | | |
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| 2D-MedVAE_KL-smooth-kuma-10K | KL-VAE | 10k | | | | | |
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| 2D-MedVAE_KL-sharp-kuma-10K | KL-VAE | 10k | | | | | |
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| 2D-MedVAE_KL-sharp-kuma-20K | KL-VAE | 20k | | | | | |
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| 2D-MedVAE_KL-sharp-kuma-100K | KL-VAE | 100k | | | | | |
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| 2D-MedVAE_KL-sharp-kuma-300K | KL-VAE | 300k | | | | | |
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| 3D-MedVAE_MAISI | KL-VAE | 40K | | | | | |
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| 3D-MedVAE_KL-sharp-kuma-100K (ongoing) | KL-VAE | 100K | | | | | |
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| 3D-MedVAE_KL-sharp-kuma-1M (ongoing) | KL-VAE | 1M | | | | | |
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| 3D-MedVAE_VQ | VQ-VAE | 100K | | | | | |
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| 3D-MedVAE_VQ-kuma-100K (ongoing) | VQ-VAE | 100K | | | | | |
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# Citation |
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``` |
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@article{liu2025see, |
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title={See More, Change Less: Anatomy-Aware Diffusion for Contrast Enhancement}, |
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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}, |
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journal={arXiv preprint arXiv:https://www.arxiv.org/abs/2512.07251}, |
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year={2025}, |
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url={https://github.com/MrGiovanni/SMILE} |
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} |
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``` |
