Image Segmentation
MLX
English
native-mlx
apple-silicon
medical-imaging
mri
brain-tumor
segmentation
research
Instructions to use MedMLX/segresnet-brats-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use MedMLX/segresnet-brats-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download MedMLX/segresnet-brats-mlx --local-dir segresnet-brats-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download reference-comparison.json from MedMLX/segresnet-brats-mlx: direct link, hf CLI and curl.
- Browser
- Download file 2.76 kB
-
https://huggingface.co/MedMLX/segresnet-brats-mlx/resolve/main/reference-comparison.json
- Command line
-
hf download hf://MedMLX/segresnet-brats-mlx/reference-comparison.json
-
curl -L -o reference-comparison.json https://huggingface.co/MedMLX/segresnet-brats-mlx/resolve/main/reference-comparison.json
2.76 kB
| { | |
| "schema_version": "medmlx.reference.v1", | |
| "model": "segresnet-brats", | |
| "date": "2026-10-12", | |
| "case": { | |
| "dataset": "MSD Task01 BrainTumour (training)", | |
| "case_id": "BRATS_001", | |
| "input_sha256": { | |
| "t1c_path": "96abcdbb541c4af37f03bef466eee5d11a3f91b9a1ea10b1a4a22cb38b7b3978", | |
| "t1_path": "740b0adfa8de83fcbdfa5cd5f14689f6d947120bfbf057fe82954c07477bb173", | |
| "t2_path": "8e2de5df0abd8b25ac1136c6570a6d03f2255107ef1265145b606f07acd1f1af", | |
| "flair_path": "7571148ce691dccff5a84109091f43385b7c069a99cd7a967b2002aaeec64cdd" | |
| } | |
| }, | |
| "original": { | |
| "code": "MONAI / MONAI model-zoo brats_mri_segmentation", | |
| "version_or_commit": "MONAI 1.6.0 (eccefc57550b111ed781d82249dfe77872a0e918); model-zoo db9eeeef7ba64ce2ced0230d9bd4c524f69b7f0c", | |
| "device": "cpu", | |
| "precision": "float32 (AMP disabled on CPU)", | |
| "checkpoint_sha256": "860ccb3f1c21c99d0410ad8a1ac4ef6b8fab60cec0a503b0ba42675741a750ae", | |
| "command": "<paper-repo>/work/reference/segresnet-brats/.venv/bin/python -I -B <paper-repo>/benchmarks/reference/segresnet-brats.py" | |
| }, | |
| "mlx": { | |
| "run_record": "results/runs/segresnet-brats.json", | |
| "output_path": "<paper-repo>/work/runs/segresnet-brats/out/brats_mri_segmentation_mask.nii.gz", | |
| "output_sha256": "ce7f2ca25eeb2e1dea09e748dfdf1ec666a16296bc523a0b537608575ed5dcd6" | |
| }, | |
| "comparison": { | |
| "metric": "minimum_per_label_dice", | |
| "value": 1.0, | |
| "per_label": { | |
| "0": { | |
| "dice": 1.0, | |
| "original_voxels": 8791633, | |
| "mlx_voxels": 8791633 | |
| }, | |
| "1": { | |
| "dice": 1.0, | |
| "original_voxels": 27348, | |
| "mlx_voxels": 27348 | |
| }, | |
| "2": { | |
| "dice": 1.0, | |
| "original_voxels": 74690, | |
| "mlx_voxels": 74690 | |
| }, | |
| "4": { | |
| "dice": 1.0, | |
| "original_voxels": 34329, | |
| "mlx_voxels": 34329 | |
| } | |
| }, | |
| "threshold": 0.99, | |
| "differing_voxel_count": 0, | |
| "result": "pass" | |
| }, | |
| "notes": "Pinned original MONAI CPU SegResNet and full bundle preprocessing, SlidingWindowInferer and postprocessing. Channel order T1gd,T1w,T2w,FLAIR is exactly the recorded MLX order; case.input_sha256 contains each recorded role's checksum. Original model.pt was found in the recorded HF snapshot 370f7f9d062745fbac445e7fe6d6616d35df04ec and matches source_sha256. Sigmoid, inverse transform, threshold 0.5 and original BraTS label precedence 4 > 1 > 2 used. Dice covers every label including background; all MRI grids and both mask affines/shapes match. AMP is disabled for CPU float32 inference. Raw outputs, source configuration, dependency versions and log: work/reference/segresnet-brats/. Training-exposed MSD case; this is reproducibility evidence, not an accuracy study.", | |
| "failure": null | |
| } | |