| { |
| "schema_version": "medmlx.run.v1", |
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| "date": "2026-10-12", |
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| "name": "segresnet-mlx", |
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| "kwargs": { |
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| "venous_path": "<paper-repo>/work/runs/inputs/renal-cect/prepared_denoised/venous.nii.gz", |
| "excretory_path": "<paper-repo>/work/runs/inputs/renal-cect/prepared_denoised/excretory.nii.gz", |
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| "process": "fresh Python process per model", |
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| "inputs": [ |
| { |
| "role": "arterial_path", |
| "path": "<paper-repo>/work/runs/inputs/renal-cect/prepared_denoised/arterial.nii.gz", |
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| "dataset": "CPTAC-CCRCC (TCIA), patient C3L-01458, CC BY 4.0", |
| "license": "Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/)", |
| "collection_doi": "https://doi.org/10.7937/k9/tcia.2018.oblamn27", |
| "patient_id": "C3L-01458", |
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| "series_description": "ARTERIAL", |
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| "source_url": "https://services.cancerimagingarchive.net/nbia-api/services/v1/getImage?SeriesInstanceUID=1.3.6.1.4.1.14519.5.2.1.6450.3304.276941581005689097417793544344", |
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| "derivation": "dcm2niix -> registration to venous grid (arterial/excretory) -> kidney crop -> median + curvature anisotropic diffusion" |
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| { |
| "role": "venous_path", |
| "path": "<paper-repo>/work/runs/inputs/renal-cect/prepared_denoised/venous.nii.gz", |
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| "license": "Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/)", |
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| "patient_id": "C3L-01458", |
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| "series_description": "VENOUS", |
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| "source_url": "https://services.cancerimagingarchive.net/nbia-api/services/v1/getImage?SeriesInstanceUID=1.3.6.1.4.1.14519.5.2.1.6450.3304.206210010421226090542971714778", |
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| "derivation": "dcm2niix -> registration to venous grid (arterial/excretory) -> kidney crop -> median + curvature anisotropic diffusion" |
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| { |
| "role": "excretory_path", |
| "path": "<paper-repo>/work/runs/inputs/renal-cect/prepared_denoised/excretory.nii.gz", |
| "sha256": "31a43dd92124042b7ac612f72ee117f87dd1755045aaa580a7a66c43a90f4c7a", |
| "dataset": "CPTAC-CCRCC (TCIA), patient C3L-01458, CC BY 4.0", |
| "license": "Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/)", |
| "collection_doi": "https://doi.org/10.7937/k9/tcia.2018.oblamn27", |
| "patient_id": "C3L-01458", |
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| "series_description": "4 MIN DELAY", |
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| "derivation": "dcm2niix -> registration to venous grid (arterial/excretory) -> kidney crop -> median + curvature anisotropic diffusion" |
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| ], |
| "outputs": { |
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| "path": "<paper-repo>/work/runs/renal-numerics-20261012/mlx-final/renalStructures_CECT_segmentation_mask.nii.gz", |
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| { |
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| { |
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| { |
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| { |
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| { |
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| { |
| "name": "source_exported_mask_comparison_completed", |
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| { |
| "name": "released_checkpoint_sparse_raw_logit_qualification", |
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| { |
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| "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", |
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| "panel": "paper/figures/runs/segresnet-renal-cect.png", |
| "status": "success", |
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| "notes": "Source-order FP32 convolution and GroupNorm resolve the retained numerical discrepancy. All 85,089,840 full-case logits and 5,935,600 mask voxels match the fresh independent MONAI CPU pipeline exactly. Anatomical accuracy remains unverified; preparation and the source model's predictions are unchanged. Historical failed records are preserved.", |
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| "qualification_status": "passed_released_checkpoint_and_retained_case_raw_logits", |
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| "affine_stop": "RegularStepGradientDescentOptimizerv4: Step too small after 24 iterations. Current step (0.000814207) is less than minimum step (0.001).", |
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| "kidney_crop": "x 70:450, y 110:330, all 71 slices of the 512x512x71 venous grid -> 380x220x71 at 0.703x0.703x2.5 mm (bundle README preprocessing step 1; chosen by eye on the venous phase)", |
| "denoise": "median radius 1 then CurvatureAnisotropicDiffusion (time step 0.04, conductance 3, 5 iterations); a substitute for the bundle's median + non-local-means (SimpleITK PatchBasedDenoising did not finish in >10 min)", |
| "package_preprocessing": "inside run(): reorient to LPS, resample to 0.8 mm, clip/scale [-1000,1000] HU to [0,1]; channel order arterial, venous, excretory", |
| "scripts": [ |
| "work/runs/_harness/prep_renal_cect.py", |
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| ] |
| }, |
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| { |
| "schema_version": "medmlx.run.v1", |
| "model": "segresnet-renal-cect", |
| "configuration": "MONAI renalStructures_CECT_segmentation (SegResNet, 3-phase CECT)", |
| "date": "2026-10-09", |
| "package": { |
| "name": "segresnet-mlx", |
| "path": "<repositories>/segresnet-mlx", |
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| "remote": "https://github.com/MedMLX/segresnet-mlx.git" |
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| "command": { |
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| "bundle": "renalStructures_CECT_segmentation", |
| "arterial_path": "<paper-repo>/work/runs/inputs/renal-cect/prepared_denoised/arterial.nii.gz", |
| "venous_path": "<paper-repo>/work/runs/inputs/renal-cect/prepared_denoised/venous.nii.gz", |
| "excretory_path": "<paper-repo>/work/runs/inputs/renal-cect/prepared_denoised/excretory.nii.gz", |
| "weights_path": "<paper-repo>/work/runs/weights/segresnet-renal-cect.safetensors", |
| "output_dir": "<paper-repo>/work/runs/segresnet-renal-cect", |
| "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-renal-cect.safetensors", |
| "sha256": "b45d0e917429f43aae433ba621099e0813550ea348f6389e4fe68f04765580ad", |
| "source": "huggingface.co/MONAI/renalStructures_CECT_segmentation@8ccbcd95 models/model.pt (radnn checkpoints copy; registry sha matches)", |
| "source_path": "<repositories>/radnn/checkpoints/monai/model-zoo/renalStructures_CECT_segmentation/models/model.pt", |
| "source_sha256": "ef934ae5b6fdf9a19b83ab9ec28b64d63dca43aea8ef3e177724c3076170a21f", |
| "registry_sha256_match": true, |
| "conversion": "segresnet_mlx.convert_checkpoint(spec=BUNDLES['renalStructures_CECT_segmentation'].spec)" |
| }, |
| "inputs": [ |
| { |
| "role": "arterial_path", |
| "path": "<paper-repo>/work/runs/inputs/renal-cect/prepared_denoised/arterial.nii.gz", |
| "sha256": "ce70fa42b63eb44df2d1e4906c61eca23ba80c748ee5358137452b4d5aeed6ff", |
| "dataset": "CPTAC-CCRCC (TCIA), patient C3L-01458, CC BY 4.0", |
| "license": "Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/)", |
| "collection_doi": "https://doi.org/10.7937/k9/tcia.2018.oblamn27", |
| "patient_id": "C3L-01458", |
| "study_instance_uid": "1.3.6.1.4.1.14519.5.2.1.6450.3304.327311208844769188742356824359", |
| "study_date": "2009-02-07", |
| "series_instance_uid": "1.3.6.1.4.1.14519.5.2.1.6450.3304.276941581005689097417793544344", |
| "series_description": "ARTERIAL", |
| "dicom_image_count": 71, |
| "source_url": "https://services.cancerimagingarchive.net/nbia-api/services/v1/getImage?SeriesInstanceUID=1.3.6.1.4.1.14519.5.2.1.6450.3304.276941581005689097417793544344", |
| "source_zip_sha256": "25a0ee8cbe751c50c2cff0436f9cabab49303a4ecb570327be5d7937691eb57e", |
| "source_dicom_dir_sha256": "3d16c3f83111a943f6a8136509d27cd71f6f95871f423023d27cd51bedcd76b7", |
| "nifti_after_dcm2niix_sha256": "aa3992e81e4fed399db5f5896be5538b3df4a2a318b0eb45533238b1f8872184", |
| "derivation": "dcm2niix -> registration to venous grid (arterial/excretory) -> kidney crop -> median + curvature anisotropic diffusion" |
| }, |
| { |
| "role": "venous_path", |
| "path": "<paper-repo>/work/runs/inputs/renal-cect/prepared_denoised/venous.nii.gz", |
| "sha256": "27673abe6398be15f0b35629e986372cae7f3128dc3e278c4939587ca72f448a", |
| "dataset": "CPTAC-CCRCC (TCIA), patient C3L-01458, CC BY 4.0", |
| "license": "Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/)", |
| "collection_doi": "https://doi.org/10.7937/k9/tcia.2018.oblamn27", |
| "patient_id": "C3L-01458", |
| "study_instance_uid": "1.3.6.1.4.1.14519.5.2.1.6450.3304.327311208844769188742356824359", |
| "study_date": "2009-02-07", |
| "series_instance_uid": "1.3.6.1.4.1.14519.5.2.1.6450.3304.206210010421226090542971714778", |
| "series_description": "VENOUS", |
| "dicom_image_count": 71, |
| "source_url": "https://services.cancerimagingarchive.net/nbia-api/services/v1/getImage?SeriesInstanceUID=1.3.6.1.4.1.14519.5.2.1.6450.3304.206210010421226090542971714778", |
| "source_zip_sha256": "6113c77d6b2d4efeafca7fa54149063fe8b3db98cb7f04d43e5ad7ce63c5309c", |
| "source_dicom_dir_sha256": "358b5f4c1eb08875778016cb4c15eaf51460e34422f632caa7a4d9219f29ce08", |
| "nifti_after_dcm2niix_sha256": "acc2ec2ad6f1fef56f6def4e3a82b7d4f0b6bcaf4c4666e764fdfb28617d4330", |
| "derivation": "dcm2niix -> registration to venous grid (arterial/excretory) -> kidney crop -> median + curvature anisotropic diffusion" |
| }, |
| { |
| "role": "excretory_path", |
| "path": "<paper-repo>/work/runs/inputs/renal-cect/prepared_denoised/excretory.nii.gz", |
| "sha256": "31a43dd92124042b7ac612f72ee117f87dd1755045aaa580a7a66c43a90f4c7a", |
| "dataset": "CPTAC-CCRCC (TCIA), patient C3L-01458, CC BY 4.0", |
| "license": "Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/)", |
| "collection_doi": "https://doi.org/10.7937/k9/tcia.2018.oblamn27", |
| "patient_id": "C3L-01458", |
| "study_instance_uid": "1.3.6.1.4.1.14519.5.2.1.6450.3304.327311208844769188742356824359", |
| "study_date": "2009-02-07", |
| "series_instance_uid": "1.3.6.1.4.1.14519.5.2.1.6450.3304.187333619377884523543352490654", |
| "series_description": "4 MIN DELAY", |
| "dicom_image_count": 180, |
| "source_url": "https://services.cancerimagingarchive.net/nbia-api/services/v1/getImage?SeriesInstanceUID=1.3.6.1.4.1.14519.5.2.1.6450.3304.187333619377884523543352490654", |
| "source_zip_sha256": "65d76ee2f4815cf19d955d1d24b1fca36e8ab3c9b79bc415783ff6719b0c44d7", |
| "source_dicom_dir_sha256": "8bb1256f43b42ed2b52636774c52ee6462ae7c3b4e8817d179a4274e8155f114", |
| "nifti_after_dcm2niix_sha256": "2a6352aad8250e227b85a44d5713f5e75253135b97bbfda37896e033b0419410", |
| "derivation": "dcm2niix -> registration to venous grid (arterial/excretory) -> kidney crop -> median + curvature anisotropic diffusion" |
| } |
| ], |
| "outputs": { |
| "mask": { |
| "path": "<paper-repo>/work/runs/segresnet-renal-cect/renalStructures_CECT_segmentation_mask.nii.gz", |
| "sha256": "eb06d7f179074a1172756993b855d5d9a7ae88e4d51addebaf2e1b5f52813821", |
| "label_voxel_counts": { |
| "0": 5563122, |
| "1": 17709, |
| "2": 4320, |
| "3": 56070, |
| "4": 5609, |
| "5": 288770 |
| }, |
| "volume_mL": { |
| "artery": 21.9, |
| "vein": 5.3, |
| "ureter": 69.3, |
| "neoplasm": 6.9, |
| "parenchyma": 356.9 |
| }, |
| "fraction_in_kidney_boxes": { |
| "artery": 0.146, |
| "vein": 0.602, |
| "ureter": 0.398, |
| "neoplasm": 0.421, |
| "parenchyma": 0.431 |
| } |
| }, |
| "details": { |
| "labels_present": [ |
| 0, |
| 1, |
| 2, |
| 3, |
| 4, |
| 5 |
| ], |
| "input_shape": [ |
| 380, |
| 220, |
| 71 |
| ], |
| "network_input_shape": [ |
| 334, |
| 193, |
| 220 |
| ], |
| "sliding_window": { |
| "roi_size": [ |
| 96, |
| 96, |
| 96 |
| ], |
| "sw_batch_size": 4, |
| "overlap": 0.25 |
| }, |
| "seconds": { |
| "load_inputs_and_weights": 0.9165, |
| "preprocess": 2.0619, |
| "inference": 15.7734, |
| "postprocess_and_write": 1.9687 |
| } |
| } |
| }, |
| "checks": [ |
| { |
| "name": "mask_reopens", |
| "result": "pass", |
| "detail": { |
| "dtype": "float32", |
| "shape": [ |
| 380, |
| 220, |
| 71 |
| ] |
| } |
| }, |
| { |
| "name": "finite_integer_labels", |
| "result": "pass", |
| "detail": null |
| }, |
| { |
| "name": "shape_matches_input", |
| "result": "pass", |
| "detail": { |
| "input": [ |
| 380, |
| 220, |
| 71 |
| ], |
| "mask": [ |
| 380, |
| 220, |
| 71 |
| ] |
| } |
| }, |
| { |
| "name": "affine_matches_input", |
| "result": "pass", |
| "detail": { |
| "max_abs_diff": 0.0 |
| } |
| }, |
| { |
| "name": "labels_in_expected_set", |
| "result": "pass", |
| "detail": { |
| "observed": [ |
| 0, |
| 1, |
| 2, |
| 3, |
| 4, |
| 5 |
| ], |
| "allowed": [ |
| 0, |
| 1, |
| 2, |
| 3, |
| 4, |
| 5 |
| ] |
| } |
| }, |
| { |
| "name": "foreground_nonempty", |
| "result": "pass", |
| "detail": { |
| "foreground_voxels": 372478 |
| } |
| }, |
| { |
| "name": "grid_matches_reference_phase_venous", |
| "result": "pass", |
| "detail": { |
| "venous_shape": [ |
| 380, |
| 220, |
| 71 |
| ], |
| "mask_shape": [ |
| 380, |
| 220, |
| 71 |
| ], |
| "affine_max_abs_diff": 0.0 |
| } |
| }, |
| { |
| "name": "renal_labels_in_kidneys", |
| "result": "fail", |
| "detail": { |
| "criterion": "at least 80% of parenchyma (label 5) voxels inside two hand-placed kidney boxes (x 20-160 and 210-360, y 55-170, z 0-62 of the 380x220x71 input grid)", |
| "fraction_in_kidney_boxes": { |
| "artery": 0.146, |
| "vein": 0.602, |
| "ureter": 0.398, |
| "neoplasm": 0.421, |
| "parenchyma": 0.431 |
| }, |
| "volume_mL": { |
| "artery": 21.9, |
| "vein": 5.3, |
| "ureter": 69.3, |
| "neoplasm": 6.9, |
| "parenchyma": 356.9 |
| }, |
| "observation": "parenchyma/ureter/vessel labels are scattered over liver, spleen, muscle, bowel and vertebral bone; kidneys are only partly covered" |
| } |
| } |
| ], |
| "duration_s": 20.77, |
| "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": 5.756, |
| "memory_definition": "mx.get_peak_memory() after mx.reset_peak_memory() immediately before run(); decimal GB", |
| "panel": "paper/figures/runs/segresnet-renal-cect-before-fix.png", |
| "status": "failure", |
| "failure_cause": "output check failed: renal_labels_in_kidneys", |
| "notes": "Run completes and writes a valid on-grid mask, but the labels are not a usable renal segmentation. Preprocessing: see extra.preparation. Package warning: transforms.py:296 RuntimeWarning overflow in exp in sigmoid (harmless: saturates to 0). Attempts on the same patient are in extra.attempts; the recorded run is the last. Not clinical.", |
| "preparation": { |
| "patient_selection_rule": "Fixed before any model output: scan collections in the order C4KC-KiTS, TCGA-KIRC, TCGA-KIRP, CPTAC-CCRCC (TCIA getSeries, Modality=CT), patients in sorted ID order; take the first patient with one study holding separate axial arterial, venous/portal and delayed/excretory series (by SeriesDescription).", |
| "search_result": "C4KC-KiTS: studies are noncontrast/arterial/'late' only (no separate venous and delayed). TCGA-KIRC/TCGA-KIRP: at most arterial + nephrographic/'Delay Nephro' (two contrast phases) per study. CPTAC-CCRCC: first match in sorted order = C3L-01458 (ARTERIAL, VENOUS, 4 MIN DELAY, plus UNENHANCED). TCGA-KICH also had one 3-phase case (TCGA-KM-8441) but was not in the rule.", |
| "conversion": "dcm2niix v1.0.20260724 (-z y), HU preserved (int16)", |
| "registration": "SimpleITK 2.5.6; fixed = venous (unchanged); moving = arterial, delayed. Euler3D rigid then affine, Mattes MI (50 bins, 20% random sampling, seed 1, body mask), 3-level pyramid, registration images clamped to [-200, 400] HU; transform = best full-sampling MI of identity/rigid/affine (arterial: affine; delayed: rigid); linear resampling onto the venous grid, -1024 HU outside.", |
| "registration_report": { |
| "fixed": "venous", |
| "metric": "Mattes MI (50 bins, 20% random sampling, seed 1, fixed body mask HU>-300); NCC on body mask, HU clipped to [-200,400]", |
| "registrations": [ |
| { |
| "moving": "arterial", |
| "ncc_identity": 0.8082911835308257, |
| "rigid_stop": "RegularStepGradientDescentOptimizerv4: Step too small after 31 iterations. Current step (0.000797923) is less than minimum step (0.001).", |
| "affine_stop": "RegularStepGradientDescentOptimizerv4: Step too small after 23 iterations. Current step (0.000814207) is less than minimum step (0.001).", |
| "mi_metric_full": { |
| "identity": -0.644816794828831, |
| "rigid": -0.6912422330256605, |
| "affine": -0.7051755570784678 |
| }, |
| "selected": "affine", |
| "selection_rule": "lowest full-sampling Mattes MI metric over identity/rigid/affine", |
| "rigid_params_euler_rad_then_mm": [ |
| 0.0056508383764504976, |
| 0.002343669555555565, |
| -0.0011454668240039366, |
| -0.2570590771572359, |
| -0.3774074675497483, |
| -0.8838321401458896 |
| ], |
| "rigid_center_mm": [ |
| 8.748443603515625, |
| -0.3515625, |
| -217.5, |
| 0.0 |
| ], |
| "affine_matrix": [ |
| 0.996809179646179, |
| 0.003656404071089731, |
| -0.00044015364500102664, |
| -0.0003091213578136837, |
| 1.0043418715160786, |
| -0.0031618346520931413, |
| -0.005001929707088511, |
| 0.005074753488242583, |
| 0.9840177451335207 |
| ], |
| "affine_translation_mm": [ |
| -0.32442920771278827, |
| -0.5139241483157303, |
| -0.9535384254277904 |
| ], |
| "affine_center_mm": [ |
| 8.748443603515625, |
| -0.3515625, |
| -217.5 |
| ], |
| "ncc_selected": 0.8294820231833323, |
| "mask_voxels": 9415516, |
| "output": "arterial" |
| }, |
| { |
| "moving": "delay", |
| "ncc_identity": 0.8346221831405791, |
| "rigid_stop": "RegularStepGradientDescentOptimizerv4: Step too small after 23 iterations. Current step (0.000797923) is less than minimum step (0.001).", |
| "affine_stop": "RegularStepGradientDescentOptimizerv4: Step too small after 24 iterations. Current step (0.000814207) is less than minimum step (0.001).", |
| "mi_metric_full": { |
| "identity": -0.7436560751665575, |
| "rigid": -0.7640989222838821, |
| "affine": -0.7447032018198703 |
| }, |
| "selected": "rigid", |
| "selection_rule": "lowest full-sampling Mattes MI metric over identity/rigid/affine", |
| "rigid_params_euler_rad_then_mm": [ |
| -0.0014328554927025279, |
| -0.0016714343257006612, |
| -0.0006300420690045122, |
| -0.07712311013748022, |
| -0.14258548962052492, |
| -0.47614984858695375 |
| ], |
| "rigid_center_mm": [ |
| 8.748443603515625, |
| -0.3515625, |
| -217.5, |
| 0.0 |
| ], |
| "affine_matrix": [ |
| 1.0058799569246073, |
| 0.0008686112874522285, |
| -0.002171033943256529, |
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| 1.008134318165822, |
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| 0.0017248076262913919, |
| -0.0046946122732977975, |
| 1.005512891291459 |
| ], |
| "affine_translation_mm": [ |
| -0.0945802475750815, |
| -0.37995403277841416, |
| -0.2653121855141817 |
| ], |
| "affine_center_mm": [ |
| 8.748443603515625, |
| -0.3515625, |
| -217.5 |
| ], |
| "ncc_selected": 0.8379312376325521, |
| "mask_voxels": 9415516, |
| "output": "excretory" |
| } |
| ] |
| }, |
| "kidney_crop": "x 70:450, y 110:330, all 71 slices of the 512x512x71 venous grid -> 380x220x71 at 0.703x0.703x2.5 mm (bundle README preprocessing step 1; chosen by eye on the venous phase)", |
| "denoise": "median radius 1 then CurvatureAnisotropicDiffusion (time step 0.04, conductance 3, 5 iterations); a substitute for the bundle's median + non-local-means (SimpleITK PatchBasedDenoising did not finish in >10 min)", |
| "package_preprocessing": "inside run(): reorient to LPS, resample to 0.8 mm, clip/scale [-1000,1000] HU to [0,1]; channel order arterial, venous, excretory", |
| "scripts": [ |
| "work/runs/_harness/prep_renal_cect.py", |
| "work/runs/_harness/prep_renal_cect_crop.py", |
| "work/runs/_harness/prep_renal_cect_denoise.py" |
| ] |
| }, |
| "attempts": [ |
| { |
| "name": "full FOV (registered, no crop/denoise)", |
| "duration_s": 79.8, |
| "mlx_peak_gb": 5.756, |
| "parenchyma_voxels": 1122691, |
| "note": "labels scattered over the whole abdomen (10,047 parenchyma components)" |
| }, |
| { |
| "name": "kidney crop, no denoise", |
| "duration_s": 17.7, |
| "mlx_peak_gb": 5.756, |
| "parenchyma_voxels": 614193, |
| "parenchyma_fraction_in_kidney_boxes": 0.457 |
| }, |
| { |
| "name": "kidney crop + denoise (recorded run)", |
| "duration_s": 20.77, |
| "mlx_peak_gb": 5.756, |
| "parenchyma_voxels": 288770, |
| "parenchyma_fraction_in_kidney_boxes": 0.431 |
| } |
| ], |
| "mlx_vs_torch_diagnostic": { |
| "description": "Torch/MONAI SegResNet 1.6.0 (CPU, float32 and float64) vs segresnet_mlx.forward_patch on the same 96^3 patch from the recorded run's preprocessed input, same checkpoint (safetensors verified byte-identical to model.pt tensors)", |
| "script": "work/runs/_harness/diagnostics/renal_cect_torch_vs_mlx_patch.py", |
| "result": "torch float32 vs float64 relative L2 7.7e-7 (the network is numerically stable) but MLX vs torch relative L2 0.638, correlation 0.52; logits are large (|ref| mean 52) in both. Stage-by-stage (renal_cect_torch_vs_mlx_bisect.py): convInit, all down stages, up_samples.*.0 and the transposed convolution match torch to ~1e-6; the unforced MLX graph then diverges from the first up-path residual block on, although running that block alone on the torch input matches (4e-7). Forcing mx.eval after individual ops/blocks changes the result (0.14, 0.04, 9e-7 or 0.45 depending on which ops were forced and on prior allocations in the process), so the divergence depends on evaluation order/buffer reuse. Not isolated further; looks like an MLX 0.32.3 or medmlx-core op bug (stale/aliased buffer), not a preprocessing issue. The package's recorded qualification is synthetic-weight only." |
| }, |
| "key_check": "run completes; mask on input grid; labels 0-5; parenchyma only 43% inside kidney boxes (fails); MLX vs MONAI torch logits disagree on the same patch (rel L2 0.64)", |
| "figure_use": "Do not use as a segmentation exemplar; usable only as an illustration of the three-phase input contract and of the failed parity check." |
| } |
| ], |
| "repair": { |
| "validated_package": { |
| "name": "medmlx-core", |
| "path": "<home>/Documents/Codex/2026-10-09/new-chat/work/renal-core-typed-validation", |
| "git_revision": "d8f4c05248fc27200e3ee9a3c7ba326d7d60b3a8", |
| "branch": "codex/renal-deconv-typed-fix", |
| "dirty": false, |
| "remote": "https://github.com/MedMLX/medmlx-core.git" |
| }, |
| "executed_sources": { |
| "medmlx-core": { |
| "path": "<home>/Documents/Codex/2026-10-09/new-chat/work/renal-core", |
| "revision": "8d690574cdecc7fa1813680fae3e3fe8c934aa37", |
| "branch": "codex/renal-deconv-fix", |
| "status": "M src/medmlx_core/ops.py\n?? docs/\n?? scripts/reproduce_transpose_consumer.py\n?? tests/test_transpose_convolution.py", |
| "diff_sha256": "b9276d78bbca6abc2967b05068aa1c984f9a0b526ed257fa01396af4b64fadb3" |
| }, |
| "segresnet-mlx": { |
| "path": "<home>/Documents/Codex/2026-10-09/new-chat/work/renal-segresnet", |
| "revision": "40cbce54523632fbc8cdfc564a9cce48d8df3424", |
| "branch": "codex/renal-kernel-fix", |
| "status": "", |
| "diff_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855" |
| } |
| }, |
| "typed_branch_operator_body_matches_executed_patch": true, |
| "production_lines_added": 11, |
| "checks": { |
| "core_suite": "220 passed", |
| "segresnet_suite": "99 passed, 2 optional checkpoint tests skipped", |
| "enabled_checkpoint_suite": "1 passed, sparse case failed", |
| "mlx_generate_consumer_suite": "223 passed", |
| "ruff": "passed", |
| "strict_typecheck": "passed" |
| } |
| }, |
| "real_patch_comparison": { |
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