Research line · dominant
Medical: pixel space
Generate or reconstruct medical images directly in pixels, with no learned latent.
What defines membership
For inverse problems the solver needs an invertible image-to-measurement mapping, so a latent is not merely unnecessary but structurally disallowed.
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
CSGM-MRI · 2021-08core
A score-based prior for compressed-sensing MRI that generalizes out of distribution where supervised reconstruction fails.
Score-based MRI recon · 2021-10landmark
Alternates score-model sampling with k-space data consistency; the projection step is why this cannot move to a latent.
Evidence
Ktena et al. · 2024-04landmark
Pixel-space cascaded diffusion improves diagnostic accuracy and subgroup fairness across three modalities — the strongest downstream evidence in the whole medical section.
MedEdit · 2024-07strong-followup
A pixel-space denoising model that induces pathology into healthy brain MRI. Its evaluation is the notable part: a blinded neuroradiologist could not distinguish the generated strokes from real ones.
What it gets right
- Required for inverse problems where measurements must stay consistent
- No tokenizer means no reconstruction ceiling and no train/test latent mismatch
- Carries the strongest downstream clinical evidence to date
Where it is weak
- Expensive at 3D volume resolutions, which is why volumetric work left it
- The choice is rarely argued in 2D work — it is usually inherited, not reasoned