Downloads · 30 days
0
MICCAI-METS_challenge_bi-intel_model
keramik05/MICCAI-METS_challenge_bi-intel_model
MICCAI-METS_challenge_bi-intel_model is a machine learning model from keramik05. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as other.
Fine-tuned nnU-Net v2 checkpoints for brain metastases segmentation. Code, inference pipeline and full method description: https://github.com/thekeramik05/MICCAI-METSchallengebi-intel
Access
Public
Updated Aug 13, 2026
Repo size
5.3 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.zip2.5 GB · 100%
At a glance
- License
- other
- Access
- Public
- Created
- Jul 30, 2026
- Updated
- Aug 13, 2026
- SHA
- 4a829102
Model card
From the Hugging Face model README
MICCAI BraTS-METS 2026 — Task 1 checkpoints
Fine-tuned nnU-Net v2 checkpoints for brain metastases segmentation. Code, inference pipeline and full method description: https://github.com/thekeramik05/MICCAI-METS_challenge_bi-intel
Contents
weights.zip contains 7 fold checkpoints only:
| Model | Trainer | Folds |
|---|---|---|
| A ("Triad") | nnUNetTrainerBraTS_TriadInit_SmallLesionWeightedCE_CEw3__3_FT900 | 0–4 |
| B ("BrainIAC") | nnUNetTrainerBraTS_BrainIACWrapper_RCOversample__3 | 1, 3 |
plus the matching dataset.json / plans.json.
The Triad and BrainIAC foundation checkpoints are not redistributed here.
Obtain them from their authors and set TRIAD_CKPT / BRAINIAC_CKPT — see §3.1
of the GitHub README. BRAINIAC_CKPT is required at inference time as well as
during training.
What Model B actually contains
Model B checkpoints are not standalone nnU-Net weights. Each embeds a complete, frozen copy of the BrainIAC ViT-B/16 backbone:
| Module prefix | Parameters | Origin |
|---|---|---|
base_network.* | 88.2 M | nnU-Net, trained here |
brainiac.* | 116.7 M | BrainIAC ViT-B/16, verbatim |
proj.* | 3 K | projection layer, trained here |
| total | 204.9 M |
Model A contains no third-party weights: the Triad checkpoint initialised the encoder, which was then fully fine-tuned, leaving a plain 88.2 M nnU-Net.
Provenance
- BrainIAC — GitHub | Nature Neuroscience (2026)
- Triad — GitHub | Medical Image Analysis (2026)
- nnU-Net v2 — GitHub (Apache-2.0)
License
| Component | Terms |
|---|---|
| Model A checkpoints | Derived from Triad (MIT) — no additional restriction |
| Model B checkpoints | BrainIAC Research-Only License (see LICENSE) |
Because Model B embeds the BrainIAC backbone, use of these weights is permitted solely for non-commercial academic research and educational purposes. Commercial use — including clinical workflows, decision-support systems, healthcare operations, or any fee-bearing product or service — is prohibited without a separate written licence from Mass General Brigham, which reserves all commercial rights.
- License
- other
- Created
- Jul 30, 2026
- Updated
- Aug 13, 2026