Models
Search by task, anatomy or name. The command below installs every model family, and each entry has its own install command.
Missing a model? Request a port
uv tool install --python 3.13 "medmlx[all,mcp]"
No models match. Try another search or choose All.
Segmentation
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nnU-Net
Segmentation of liver and pancreas CT, brain tumor MRI and PET/CT lesions.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "nnunet-mlx>=0.6.4" -
TotalSegmentator
Identify 117 organs and other body structures in CT scans.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "totalsegmentator-mlx>=0.1.6" -
MedSAM2
Lesion or organ segmentation from a box, point or mask prompt, tracked through the volume.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "medsam2-mlx>=0.2.8" -
BioMedParse
Text-prompted 3D segmentation with Microsoft BioMedParse v2.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "biomedparse-mlx>=0.1.5" -
NV-Segment-CT
Automatic and prompted 3D CT segmentation with NVIDIA VISTA-3D.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "mlx-segment-ct>=0.3.5" -
SegResNet
Segmentation of brain tumors, kidney structures and whole-body anatomy from MRI or CT.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "segresnet-mlx>=0.3.3" -
UNesT
Kidney cortex, medulla and collecting system segmentation in contrast-enhanced CT.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "unest-mlx>=0.2.7" -
SwinUNETR
Organ segmentation in abdominal CT.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "swin-unetr-mlx>=0.2.6" -
SynthSeg
Brain segmentation and parcellation.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "synthseg-mlx>=0.3.4" -
FleXray
Radiograph segmentation.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "flexray-mlx>=0.2.6" -
FlexibleUNet
Identify surgical instruments in endoscopy images.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "flexible-unet-mlx>=0.2.6"
Detection
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3D RetinaNet
MONAI RetinaNet lung nodule detection in 3D chest CT.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "lung-nodule-mlx>=0.2.6"
Generation
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FDG PET/CT
Synthetic FDG PET from CT and organ labels, with scanner blur, scatter and count noise.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "fdg-pet-ct-mlx>=0.2.6" -
NV-Generate
Synthetic 3D CT and MRI generation with NVIDIA NV-Generate (MAISI).
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "mlx-generate-ctmr>=0.5.2.post2" -
LesionDiffusion
Synthetic lesions in CT, with control of organ, size, placement and phenotype.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "lesiondiffusion-mlx>=0.2.6.post2"
Vision-language
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MLX-Reason-CT
Writes a structured report for a chest or abdomen CT volume, or answers a question about it.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "mlx-reason-ct>=0.3.6" -
MedGemma
Ask questions about medical images and receive written responses on your Mac.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "med-vlm-mlx>=0.3.2"
Quantitative imaging
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PyRadiomics
Measure image intensity, texture and shape within a selected region.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "mlxradiomics>=0.1.0.dev10"
Feature extraction
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RadImageNet
Extract numerical image features for research with models trained on RadImageNet.
Install on its own
uv venv --python 3.13 source .venv/bin/activate uv pip install "radimagenet-mlx>=0.3.3"
Request a model
Have a medical imaging model you’d like to run on your Mac? Suggest it for a MedMLX port.
Share the model’s repository or paper and what you’d use it for. Links to weights and license details help us assess a port.
Requests are public and require a GitHub account. If your model is already requested, add your use case to that thread. A request does not guarantee a port or a release date.