Research line · ascendant
Medical: domain-trained latent
Train an autoencoder on medical data itself, then run diffusion in that latent.
What defines membership
A latent is necessary for 3D volumes, but it must be trained on the target anatomy rather than borrowed from natural images.
How the line developed
Read top to bottom: what came before, the idea itself, the evidence for it, what improved, and where it breaks.
The idea
Pinaya brain-MRI LDM · 2022-09core
Latent diffusion for 3D brain MRI conditioned on age, sex and structure volumes, with 100,000 synthetic brains released.
Evidence
MONAI Generative Models · 2023-07core
Turns the recipe into shared infrastructure most later medical generative work builds on.
At scale
MAISI · 2024-09core
3D CT at 512-cubed via a domain-trained VAE-GAN compressing to 128-cubed, with the memory constraint stated outright as the reason.
What it gets right
- The only practical route to full 3D volumes at clinical resolution
- Latent statistics match the anatomy rather than natural photographs
- Explicitly reasoned: the memory argument is stated, not assumed
Where it is weak
- Needs enough in-domain data to train an autoencoder, which many modalities lack
- Demonstrated memorization risk in the 3D latent setting
- Little downstream clinical evidence compared with the pixel-space line