LUMINR: Intensity-conditioned Meta-learned INRs for Accelerated Medical Image Registration
Implicit neural representations (INRs) have been applied to deformable image registration, where a lightweight network maps a spatial coordinate to a deformation vector. Because an INR encodes one specific signal in its parameters, a fresh network must be optimized for every image pair at test time, which is slow. Existing methods reduce this cost by meta-learning an initialization across a population of image pairs, and condition the weights of the INR globally, so the same conditioning applies at every coordinate while the deformation field it initializes varies across the domain. We propose LUMINR, a meta-learning framework that accelerates this per-pair fitting and in which the condit...
| Authors | Damian Rynczak, Marek Wodzinski |
|---|---|
| Primary category | eess.IV |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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