HKU-MedAI/GEM-3D
[IJCV'2026] Generative Enhancement for 3D Medical Images
Implements a two-stage latent diffusion framework with KL-VAE compression for 3D volumetric synthesis, supporting both full-volume generation and position-conditioned slice-by-slice reconstruction. Leverages nnUNet preprocessing and integrates pretrained LDM components, enabling flexible conditioning modes (image-to-image, guidance) across brain and abdominal datasets. Optimized for multi-GPU training (8× V100/A100) with inference requiring <20GB memory.
Stars
75
Forks
5
Language
Python
License
GPL-3.0
Category
Last pushed
Mar 12, 2026
Commits (30d)
0
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