MetaSeg-FINER 2D 5-class (OASIS, retuned omega_0=15)

Meta-learned FINER implicit neural network (Liu et al., CVPR 2024) for 5-class brain MRI segmentation on neurite-OASIS coronal slices. Plugged into the MetaSeg recipe with a single hyperparameter retune (omega_0=15, half the FINER paper default). Slightly exceeds SIREN at identical parameter count.

Performance

The retune

FINER's paper-default hyperparameters lose to SIREN by ~0.04 Dice inside the MetaSeg recipe (FINER default → 0.886, SIREN → 0.925). The activation sin(omega_0 * (|x| + 1) * x) already amplifies frequency via the |x|+1 factor; layering SIREN's default omega_0=30 on top destabilises second-order MAML gradients. Halving omega_0 to 15 closes the gap and slightly exceeds SIREN, and the same retune transfers cleanly to the 25-class task (metaseg-finer-2d-25cls would close the gap there too, given the same fix).

This is evidence that the MetaSeg recipe is genuinely INR-agnostic — but only if you treat backbone hyperparameters as part of the recipe, not as fixed defaults inherited from the upstream signal-fitting use case.

Usage

from inr_brain_seg import InrBrainSeg
model = InrBrainSeg.from_pretrained("basimazam/metaseg-finer-2d-5cls")
mask  = model.segment("path/to/T1.nii.gz")

Training details

Same as the SIREN 2D-5cls model except:

• Backbone: FINER with omega_0=15, first_bias_scale=10, scale_req_grad=False.

Citation

@inproceedings{liu2024finer,
  title     = {{FINER}: Flexible Spectral-bias Tuning in Implicit Neural Representation by Variable-periodic Activation Functions},
  author    = {Liu, Zhen and others},
  booktitle = {CVPR},
  year      = {2024}
}

License

Apache License 2.0.