Image Segmentation
MLX
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Instructions to use MedMLX/segresnet-renal-cect-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use MedMLX/segresnet-renal-cect-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download MedMLX/segresnet-renal-cect-mlx --local-dir segresnet-renal-cect-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download reference-comparison.json from MedMLX/segresnet-renal-cect-mlx: direct link, hf CLI and curl.
- Browser
- Download file 7.82 kB
-
https://huggingface.co/MedMLX/segresnet-renal-cect-mlx/resolve/main/reference-comparison.json
- Command line
-
hf download hf://MedMLX/segresnet-renal-cect-mlx/reference-comparison.json
-
curl -L -o reference-comparison.json https://huggingface.co/MedMLX/segresnet-renal-cect-mlx/resolve/main/reference-comparison.json
7.82 kB
| { | |
| "schema_version": "medmlx.reference.v1", | |
| "model": "segresnet-renal-cect", | |
| "date": "2026-10-12T23:27:50.228834+00:00", | |
| "case": { | |
| "dataset": "CPTAC-CCRCC (TCIA), patient C3L-01458, CC BY 4.0", | |
| "case_id": "C3L-01458", | |
| "input_sha256": { | |
| "arterial_path": "ce70fa42b63eb44df2d1e4906c61eca23ba80c748ee5358137452b4d5aeed6ff", | |
| "venous_path": "27673abe6398be15f0b35629e986372cae7f3128dc3e278c4939587ca72f448a", | |
| "excretory_path": "31a43dd92124042b7ac612f72ee117f87dd1755045aaa580a7a66c43a90f4c7a" | |
| } | |
| }, | |
| "original": { | |
| "code": "Project-MONAI/model-zoo/renalStructures_CECT_segmentation", | |
| "version_or_commit": "db9eeeef7ba64ce2ced0230d9bd4c524f69b7f0c", | |
| "precision": "float32; MONAI 1.6.0, PyTorch 2.14.1", | |
| "command": "MEDMLX_RENAL_WORK=<paper-repo>/work/runs/renal-numerics-20261012 MEDMLX_RENAL_BUNDLE=<paper-repo>/work/runs/renal-numerics-20261012/bundles/renalStructures_CECT_segmentation <repositories>/mlx/segresnet-mlx/.venv/bin/python <paper-repo>/benchmarks/reference/renal_cect/monai_reference.py", | |
| "command_reconstructed": false, | |
| "execution_date": "2026-10-12", | |
| "source_files_sha256": { | |
| "configs/inference.json": "a7955d3a07c5085351f29cf572361964cff0a7ae76f0363aae49eab4c849a535", | |
| "scripts/my_transforms.py": "d780252d1a30adce08d1a94cb348c1792b4f42b12fc761f5be93b1b8278a2ead", | |
| "models/model.pt": "ef934ae5b6fdf9a19b83ab9ec28b64d63dca43aea8ef3e177724c3076170a21f" | |
| }, | |
| "monai_revision": "eccefc57550b111ed781d82249dfe77872a0e918", | |
| "retained_reference_device": "torch-cpu", | |
| "device": "cpu", | |
| "checkpoint_sha256": "ef934ae5b6fdf9a19b83ab9ec28b64d63dca43aea8ef3e177724c3076170a21f" | |
| }, | |
| "mlx": { | |
| "run_record": "results/runs/segresnet-renal-cect.json", | |
| "output_path": "<paper-repo>/work/runs/renal-numerics-20261012/mlx-final/renalStructures_CECT_segmentation_mask.nii.gz", | |
| "output_sha256": "fa6de5a6a23f0216a1ca477ee75222e9599058753ec029e64a6afebca9cb3314" | |
| }, | |
| "comparison": { | |
| "metric": "minimum per-label mask Dice", | |
| "value": 1.0, | |
| "per_label": { | |
| "0": { | |
| "dice": 1.0, | |
| "original_voxels": 5412229, | |
| "mlx_voxels": 5412229, | |
| "intersection_voxels": 5412229 | |
| }, | |
| "1": { | |
| "dice": 1.0, | |
| "original_voxels": 84553, | |
| "mlx_voxels": 84553, | |
| "intersection_voxels": 84553 | |
| }, | |
| "2": { | |
| "dice": 1.0, | |
| "original_voxels": 32851, | |
| "mlx_voxels": 32851, | |
| "intersection_voxels": 32851 | |
| }, | |
| "3": { | |
| "dice": 1.0, | |
| "original_voxels": 23363, | |
| "mlx_voxels": 23363, | |
| "intersection_voxels": 23363 | |
| }, | |
| "4": { | |
| "dice": 1.0, | |
| "original_voxels": 276280, | |
| "mlx_voxels": 276280, | |
| "intersection_voxels": 276280 | |
| }, | |
| "5": { | |
| "dice": 1.0, | |
| "original_voxels": 106324, | |
| "mlx_voxels": 106324, | |
| "intersection_voxels": 106324 | |
| } | |
| }, | |
| "threshold": { | |
| "min_dice": 0.99, | |
| "scope": "every label present in either mask, including background" | |
| }, | |
| "result": "pass", | |
| "differing_voxels": 0, | |
| "geometry": { | |
| "shape": [ | |
| 380, | |
| 220, | |
| 71 | |
| ], | |
| "affine_max_abs_diff": 0.0 | |
| }, | |
| "original_output_path": "<paper-repo>/work/runs/renal-numerics-20261012/monai/export/arterial/arterial_trans.nii.gz", | |
| "original_output_sha256": "57e9025b66bbf7ba862e375c106c630baa39ff6fecc58205eb91cc038fefb1a0", | |
| "raw_logits": { | |
| "shape": [ | |
| 1, | |
| 6, | |
| 334, | |
| 193, | |
| 220 | |
| ], | |
| "dtype": "float32", | |
| "rtol": 0.0001, | |
| "atol": 0.0001, | |
| "tolerance_source": "segresnet-mlx/tests/test_torch_oracles.py; unchanged released-checkpoint criterion", | |
| "allclose": true, | |
| "signs_equal": true, | |
| "failing_elements": 0, | |
| "sign_mismatches": 0, | |
| "normalized_l2": 0.0, | |
| "channels": [ | |
| { | |
| "label": "background", | |
| "elements": 14181640, | |
| "max_abs": 0.0, | |
| "mean_abs": 0.0, | |
| "failing_elements_rtol_1e-4_atol_1e-4": 0, | |
| "sign_mismatches": 0, | |
| "finite": true | |
| }, | |
| { | |
| "label": "artery", | |
| "elements": 14181640, | |
| "max_abs": 0.0, | |
| "mean_abs": 0.0, | |
| "failing_elements_rtol_1e-4_atol_1e-4": 0, | |
| "sign_mismatches": 0, | |
| "finite": true | |
| }, | |
| { | |
| "label": "vein", | |
| "elements": 14181640, | |
| "max_abs": 0.0, | |
| "mean_abs": 0.0, | |
| "failing_elements_rtol_1e-4_atol_1e-4": 0, | |
| "sign_mismatches": 0, | |
| "finite": true | |
| }, | |
| { | |
| "label": "ureter", | |
| "elements": 14181640, | |
| "max_abs": 0.0, | |
| "mean_abs": 0.0, | |
| "failing_elements_rtol_1e-4_atol_1e-4": 0, | |
| "sign_mismatches": 0, | |
| "finite": true | |
| }, | |
| { | |
| "label": "neoplasm", | |
| "elements": 14181640, | |
| "max_abs": 0.0, | |
| "mean_abs": 0.0, | |
| "failing_elements_rtol_1e-4_atol_1e-4": 0, | |
| "sign_mismatches": 0, | |
| "finite": true | |
| }, | |
| { | |
| "label": "parenchyma", | |
| "elements": 14181640, | |
| "max_abs": 0.0, | |
| "mean_abs": 0.0, | |
| "failing_elements_rtol_1e-4_atol_1e-4": 0, | |
| "sign_mismatches": 0, | |
| "finite": true | |
| } | |
| ], | |
| "elements": 85089840, | |
| "finite": true, | |
| "max_abs": 0.0, | |
| "differing_elements": 0, | |
| "array_equal": true, | |
| "result": "pass" | |
| }, | |
| "retained_receipt_path": "results/renal-numerics-20261012.json", | |
| "retained_receipt_sha256": "fdd985ea32200b4a4c47319058c6bff62d2a57bf7e0399283235581e4b4f127d" | |
| }, | |
| "notes": "Rechecked the fresh independently parsed 2026-10-12 MONAI CPU pipeline and corrected native MLX outputs on the retained C3L-01458 preparation; this recheck performs no model inference. Source-order FP32 convolution and Welford GroupNorm yield bitwise-identical raw logits: 0/85,089,840 differences, max absolute error 0, zero sign mismatches, unchanged rtol=atol=1e-4 pass. All 5,935,600 exported mask voxels agree (every label Dice 1). Stable sigmoid eliminates the native exp overflow warning without changing threshold decisions. Anatomical accuracy remains unverified: manual crop/registration and substituted denoising, no reference labels. Historical failed evidence is retained in the linked prior records.", | |
| "failure": null, | |
| "verification": { | |
| "inputs": { | |
| "arterial_path": { | |
| "path": "<paper-repo>/work/runs/inputs/renal-cect/prepared_denoised/arterial.nii.gz", | |
| "sha256": "ce70fa42b63eb44df2d1e4906c61eca23ba80c748ee5358137452b4d5aeed6ff" | |
| }, | |
| "venous_path": { | |
| "path": "<paper-repo>/work/runs/inputs/renal-cect/prepared_denoised/venous.nii.gz", | |
| "sha256": "27673abe6398be15f0b35629e986372cae7f3128dc3e278c4939587ca72f448a" | |
| }, | |
| "excretory_path": { | |
| "path": "<paper-repo>/work/runs/inputs/renal-cect/prepared_denoised/excretory.nii.gz", | |
| "sha256": "31a43dd92124042b7ac612f72ee117f87dd1755045aaa580a7a66c43a90f4c7a" | |
| } | |
| }, | |
| "mlx_outputs": { | |
| "mask": { | |
| "path": "<paper-repo>/work/runs/renal-numerics-20261012/mlx-final/renalStructures_CECT_segmentation_mask.nii.gz", | |
| "sha256": "fa6de5a6a23f0216a1ca477ee75222e9599058753ec029e64a6afebca9cb3314" | |
| } | |
| }, | |
| "checkpoint": { | |
| "path": "<paper-repo>/work/runs/renal-numerics-20261012/bundles/renalStructures_CECT_segmentation/models/model.pt", | |
| "sha256": "ef934ae5b6fdf9a19b83ab9ec28b64d63dca43aea8ef3e177724c3076170a21f" | |
| } | |
| }, | |
| "prior_attempt_records": [ | |
| "results/reference/history/segresnet-renal-cect-before-20261012-fix.json", | |
| "results/renal-independent-reference-20261009.json" | |
| ] | |
| } | |