Metaseg Siren 2d 25cls

--- license: apache-2.0 language: en library_name: pytorch tags: - medical-imaging - brain-mri - segmentation - implicit-neural-representation - meta-learning - siren - parameter-efficient datasets: - oasis metrics: - dice pipeline_tag: image-segmentation --- # MetaSeg-SIREN 2D 25-class (OASIS) Meta-learned **SIREN implicit neural network** for 25-class brain MRI segmentation on neurite-OASIS coronal slices. Trained with the MetaSeg recipe (Vyas et al., MICCAI 2025) — 5,000 outer-loop MAML iterations + 4,001 classifier-only finetune epochs. ## Performance | Metric | Value | Source | |---|---:|---| | Mean Dice (OASIS test split, n=80) | **0.855 ± 0.035** | Reproduction of paper Table 1 row 2 (paper: 0.86 ± 0.032, within 1σ) | | INR parameters | 1.06 M | – | | Inference time (per slice, A100 BF16) | ~0.4 s | inner_steps=100 | Output classes: 1 background + 24 foreground anatomical regions (left/right cortex, left/right WM, thalamus, caudate, putamen, pallidum, hippocampus, amygdala, accumbens, brainstem, cerebellum). The 25-vs-24 difference is a naming convention: the paper counts 24 foreground labels; this checkpoint includes class 0 (background) for a total of 25 softmax bins. Both refer to the same `seg24` files in the neurite-OASIS release. ## Usage ```python from inr_brain_seg import InrBrainSeg model = InrBrainSeg.from_pretrained("basimazam/metaseg-siren-2d-25cls") mask = model.segment("path/to/T1.nii.gz") ``` ## Training details * **Backbone**: SIREN with `omega_0=30`, 3 hidden layers of width 512. * **Outer loop**: 5,000 MAML iterations, lr 1e-4, inner_steps=2. * **Classifier finetune**: 4,001 epochs, lr 5e-5. * **Test-time inner loop**: K=100 steps at lr 1e-4. Training data: 314 neurite-OASIS subjects. Validation: 21. Test: 80. ## Intended use Research only. See the 5-class card for cross-site / clinical-use caveats. ## Citation ```bibtex @inproceedings{azam2026inrbrainseg, title = {Coordinate-Field Implicit Networks for Cross-Site Brain MRI Segmentation}, author = {Azam, Basim}, booktitle = {ACCV}, year = {2026} } ``` ## License Apache License 2.0.

Source context: 6 downloads · 1 likes · Pipeline image-segmentation · Library pytorch · Repo basimazam/metaseg-siren-2d-25cls