Upload converted segresnet-brats weights and release metadata

LICENSE

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NOTICE

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+Original publisher: MONAI
+Original source: https://huggingface.co/MONAI/brats_mri_segmentation
+
+Retain the applicable license, copyright and attribution notices; identify the upstream checkpoint, authors, and MLX conversion changes.
+Include Apache-2.0, retain applicable upstream NOTICE content, and mark modifications.
+
+MLX conversion by Joseph Sandoval and Alfredo Sandoval Jr.; no retraining.

README.md

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+---
+license: apache-2.0
+library_name: mlx
+tags:
+- medical-imaging
+- apple-silicon
+- research
+language:
+- en
+---
+# MONAI brats_mri_segmentation (SegResNet, 4-channel MRI)
+
+Native MLX package: [segresnet-mlx](https://github.com/MedMLX/segresnet-mlx.git).
+Evaluated revision: `40cbce54523632fbc8cdfc564a9cce48d8df3424`.
+
+This card describes the one configuration evaluated in the MedMLX paper. It is research software; the single-case comparison does not estimate clinical accuracy.
+
+## Source and checkpoint
+
+Original publisher: MONAI.
+Original implementation: [https://huggingface.co/MONAI/brats_mri_segmentation](https://huggingface.co/MONAI/brats_mri_segmentation).
+Source revision or version: `370f7f9d062745fbac445e7fe6d6616d35df04ec`.
+Original checkpoint: https://huggingface.co/MONAI/brats_mri_segmentation/resolve/370f7f9d062745fbac445e7fe6d6616d35df04ec/models/model.pt
+
+Conversion: segresnet_mlx.convert_checkpoint(spec=BUNDLES['brats_mri_segmentation'].spec).
+Conversion changes tensor layout or storage as specified by the native package; it does not retrain the model.
+
+## Recorded example
+
+MSD Task01 BrainTumour (training) / BRATS_001; MSD Task01 BrainTumour (training) / BRATS_001; MSD Task01 BrainTumour (training) / BRATS_001; MSD Task01 BrainTumour (training) / BRATS_001
+
+```python
+from segresnet_mlx.medmlx import run
+
+# Replace file and directory names with your locally obtained inputs and weights.
+kwargs = {
+    "bundle": "brats_mri_segmentation",
+    "weights_path": "segresnet-brats.safetensors",
+    "output_dir": "out",
+    "t1c_path": "T1gd.nii.gz",
+    "t1_path": "T1w.nii.gz",
+    "t2_path": "T2w.nii.gz",
+    "flair_path": "FLAIR.nii.gz",
+    "overwrite": True
+}
+result = run(**kwargs)
+print(result["outputs"])
+```
+
+Native result: **success**. Original-code comparison: **pass**, `minimum_per_label_dice` = `1.0`.
+
+Native whole-volume output compared with the original MONAI bundle; the October 12 renal operator repair also has a full-case BraTS regression.
+
+The complete [native run](native-run.json) and [original-code comparison](reference-comparison.json) retain commands, geometry, precision, and output-specific checks. Use the [replay harness](https://github.com/MedMLX/Med-MLX/blob/main/benchmarks/README.md) to substitute local paths. The package README documents installation and conversion.
+
+## Distribution terms
+
+Upstream code: **Apache-2.0**. Weights: **Apache-2.0**.
+Converted weights are hosted in this repository.
+
+Native whole-volume output compared with the original MONAI bundle; the October 12 renal operator repair also has a full-case BraTS regression. Converted weights may retain the identified upstream license and required notices; the accompanying notices describe the redistribution terms.
+
+Full [upstream license](LICENSE) accompanies any weight distribution.
+
+Required notices:
+
+- Retain the applicable license, copyright and attribution notices; identify the upstream checkpoint, authors, and MLX conversion changes.
+- Include Apache-2.0, retain applicable upstream NOTICE content, and mark modifications.
+
+Review sources:
+
+- https://huggingface.co/MONAI/brats_mri_segmentation
+- https://huggingface.co/MONAI/brats_mri_segmentation/resolve/370f7f9d062745fbac445e7fe6d6616d35df04ec/models/model.pt
+- https://huggingface.co/MONAI/brats_mri_segmentation/blob/370f7f9d062745fbac445e7fe6d6616d35df04ec/LICENSE
+
+## Packaged files
+
+- `segresnet-brats.safetensors`

native-run.json

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+{
+  "schema_version": "medmlx.run.v1",
+  "model": "segresnet-brats",
+  "configuration": "MONAI brats_mri_segmentation (SegResNet, 4-channel MRI)",
+  "date": "2026-10-08",
+  "package": {
+    "name": "segresnet-mlx",
+    "path": "<repositories>/segresnet-mlx",
+    "git_revision": "40cbce54523632fbc8cdfc564a9cce48d8df3424",
+    "branch": "main",
+    "dirty": false,
+    "remote": "https://github.com/MedMLX/segresnet-mlx.git"
+  },
+  "environment": {
+    "machine": "MacBook Pro (2021), Apple M1 Max, 10-core CPU, 24-core GPU, 64 GB",
+    "macos": "27.0",
+    "python": "3.12.12",
+    "mlx_version": "0.32.3",
+    "device": "gpu"
+  },
+  "command": {
+    "entry_point": "segresnet_mlx.medmlx:run",
+    "kwargs": {
+      "bundle": "brats_mri_segmentation",
+      "weights_path": "<paper-repo>/work/runs/weights/segresnet-brats.safetensors",
+      "output_dir": "<paper-repo>/work/runs/segresnet-brats/out",
+      "t1c_path": "<home>/Documents/Codex/2026-10-08/https-drive-google-com-drive-folders/outputs/medmlx/datasets/qualification-2026-10-08/derived/msd-brain-channels/BRATS_001/T1gd.nii.gz",
+      "t1_path": "<home>/Documents/Codex/2026-10-08/https-drive-google-com-drive-folders/outputs/medmlx/datasets/qualification-2026-10-08/derived/msd-brain-channels/BRATS_001/T1w.nii.gz",
+      "t2_path": "<home>/Documents/Codex/2026-10-08/https-drive-google-com-drive-folders/outputs/medmlx/datasets/qualification-2026-10-08/derived/msd-brain-channels/BRATS_001/T2w.nii.gz",
+      "flair_path": "<home>/Documents/Codex/2026-10-08/https-drive-google-com-drive-folders/outputs/medmlx/datasets/qualification-2026-10-08/derived/msd-brain-channels/BRATS_001/FLAIR.nii.gz",
+      "overwrite": true
+    },
+    "invocation": "cd <repositories>/segresnet-mlx && .venv/bin/python <paper-repo>/work/runs/_harness/run_one.py segresnet_mlx.medmlx <kwargs.json> <run_result.json>",
+    "process": "fresh Python process per model"
+  },
+  "checkpoint": {
+    "path": "<paper-repo>/work/runs/weights/segresnet-brats.safetensors",
+    "sha256": "9044dcf3e2a82ef18f14d7abf507debbea52e14560c304b990a4c0e412bc9c30",
+    "source": "huggingface.co/MONAI/brats_mri_segmentation@370f7f9d models/model.pt (local HF cache)",
+    "source_sha256": "860ccb3f1c21c99d0410ad8a1ac4ef6b8fab60cec0a503b0ba42675741a750ae",
+    "registry_sha256_match": true,
+    "conversion": "segresnet_mlx.convert_checkpoint(spec=BUNDLES['brats_mri_segmentation'].spec)"
+  },
+  "inputs": [
+    {
+      "role": "t1c_path",
+      "path": "<home>/Documents/Codex/2026-10-08/https-drive-google-com-drive-folders/outputs/medmlx/datasets/qualification-2026-10-08/derived/msd-brain-channels/BRATS_001/T1gd.nii.gz",
+      "sha256": "96abcdbb541c4af37f03bef466eee5d11a3f91b9a1ea10b1a4a22cb38b7b3978",
+      "dataset": "MSD Task01 BrainTumour (training)",
+      "case_id": "BRATS_001",
+      "derivation": "channel split of imagesTr/BRATS_001.nii.gz (MSD order FLAIR,T1w,T1gd,T2w)"
+    },
+    {
+      "role": "t1_path",
+      "path": "<home>/Documents/Codex/2026-10-08/https-drive-google-com-drive-folders/outputs/medmlx/datasets/qualification-2026-10-08/derived/msd-brain-channels/BRATS_001/T1w.nii.gz",
+      "sha256": "740b0adfa8de83fcbdfa5cd5f14689f6d947120bfbf057fe82954c07477bb173",
+      "dataset": "MSD Task01 BrainTumour (training)",
+      "case_id": "BRATS_001",
+      "derivation": "channel split of imagesTr/BRATS_001.nii.gz (MSD order FLAIR,T1w,T1gd,T2w)"
+    },
+    {
+      "role": "t2_path",
+      "path": "<home>/Documents/Codex/2026-10-08/https-drive-google-com-drive-folders/outputs/medmlx/datasets/qualification-2026-10-08/derived/msd-brain-channels/BRATS_001/T2w.nii.gz",
+      "sha256": "8e2de5df0abd8b25ac1136c6570a6d03f2255107ef1265145b606f07acd1f1af",
+      "dataset": "MSD Task01 BrainTumour (training)",
+      "case_id": "BRATS_001",
+      "derivation": "channel split of imagesTr/BRATS_001.nii.gz (MSD order FLAIR,T1w,T1gd,T2w)"
+    },
+    {
+      "role": "flair_path",
+      "path": "<home>/Documents/Codex/2026-10-08/https-drive-google-com-drive-folders/outputs/medmlx/datasets/qualification-2026-10-08/derived/msd-brain-channels/BRATS_001/FLAIR.nii.gz",
+      "sha256": "7571148ce691dccff5a84109091f43385b7c069a99cd7a967b2002aaeec64cdd",
+      "dataset": "MSD Task01 BrainTumour (training)",
+      "case_id": "BRATS_001",
+      "derivation": "channel split of imagesTr/BRATS_001.nii.gz (MSD order FLAIR,T1w,T1gd,T2w)"
+    }
+  ],
+  "outputs": {
+    "mask": {
+      "path": "<paper-repo>/work/runs/segresnet-brats/out/brats_mri_segmentation_mask.nii.gz",
+      "sha256": "ce7f2ca25eeb2e1dea09e748dfdf1ec666a16296bc523a0b537608575ed5dcd6",
+      "label_voxel_counts": {
+        "0": 8791633,
+        "1": 27348,
+        "2": 74690,
+        "4": 34329
+      }
+    },
+    "details": {
+      "labels_present": [
+        0,
+        1,
+        2,
+        4
+      ],
+      "input_shape": [
+        240,
+        240,
+        155
+      ],
+      "sliding_window": {
+        "roi_size": [
+          240,
+          240,
+          160
+        ],
+        "sw_batch_size": 1,
+        "overlap": 0.5
+      },
+      "seconds": {
+        "load_inputs_and_weights": 0.3394,
+        "preprocess": 0.1134,
+        "inference": 1.4535,
+        "postprocess_and_write": 0.2894
+      }
+    }
+  },
+  "checks": [
+    {
+      "name": "mask_reopens",
+      "result": "pass",
+      "detail": {
+        "dtype": "uint8",
+        "shape": [
+          240,
+          240,
+          155
+        ]
+      }
+    },
+    {
+      "name": "finite_integer_labels",
+      "result": "pass",
+      "detail": null
+    },
+    {
+      "name": "shape_matches_input",
+      "result": "pass",
+      "detail": {
+        "input": [
+          240,
+          240,
+          155
+        ],
+        "mask": [
+          240,
+          240,
+          155
+        ]
+      }
+    },
+    {
+      "name": "affine_matches_input",
+      "result": "pass",
+      "detail": {
+        "max_abs_diff": 0.0
+      }
+    },
+    {
+      "name": "labels_in_expected_set",
+      "result": "pass",
+      "detail": {
+        "observed": [
+          0,
+          1,
+          2,
+          4
+        ],
+        "allowed": [
+          0,
+          1,
+          2,
+          4
+        ]
+      }
+    },
+    {
+      "name": "foreground_nonempty",
+      "result": "pass",
+      "detail": {
+        "foreground_voxels": 136367
+      }
+    },
+    {
+      "name": "required_labels_present",
+      "result": "pass",
+      "detail": {
+        "required": [
+          1,
+          2
+        ]
+      }
+    },
+    {
+      "name": "tumor_overlaps_annotated_region",
+      "result": "pass",
+      "detail": {
+        "whole_tumor_dice_vs_msd_label_informational": 0.8897
+      }
+    }
+  ],
+  "duration_s": 2.271,
+  "timing_boundary": "wall clock around the single run() call: weight loading, preprocessing, MLX inference and export; interpreter start-up and package import excluded; one run, no warmup",
+  "mlx_peak_gb": 6.954,
+  "memory_definition": "mx.get_peak_memory() after mx.reset_peak_memory() immediately before run(); decimal GB",
+  "panel": "paper/figures/runs/segresnet-brats.png",
+  "status": "success",
+  "failure_cause": null,
+  "notes": "BRATS_001 is training-exposed MSD data; overlap with the MSD label is a sanity check, not an accuracy claim.",
+  "key_check": "labels [1, 2, 4]; whole_tumor_dice_vs_msd_label_informational = 0.8897"
+}

reference-comparison.json

@@ -0,0 +1,59 @@
+{
+  "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
+}

segresnet-brats.safetensors

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