radimagenet-mlx
Extract radiology-image features with native MLX RadImageNet encoders: ResNet50, DenseNet121, InceptionV3 and InceptionResNetV2.
CT → embedding-based image retrieval, ranked by cosine similarity.
| Input | Output |
|---|---|
| 2D image + Keras checkpoint | Pooled and spatial features |
Apple Silicon · Metal · Python 3.13 · uv
Quick start
uv venv --python 3.13 --seed
source .venv/bin/activate
python -m pip install "radimagenet-mlx==0.2.1"
The tagged GitHub release is an alternative installation route:
repo="https://github.com/MedMLX/radimagenet-mlx.git@v0.2.1"
python -m pip install "radimagenet-mlx @ git+$repo"
Download a RadImageNet checkpoint, then extract features:
from radimagenet_mlx.medmlx import run
result = run(
image_path="slice.png",
weights_path="RadImageNet-ResNet50_notop.h5",
output_dir="features",
)
print(result["outputs"]["features_npy"])
The package contains code only and requires Python 3.13. Supply your own
upstream .h5 weights under their publisher terms; the loader reads them directly
without TensorFlow. Redistribution of converted weights remains unresolved.
Classifier predictions require a checkpoint that includes a classifier.
License
Apache-2.0. See third-party notices. Model weights retain their publisher terms.
For research and education. Outputs require human review.
1 maintainer
Metadata
1 maintainer
Release files for radimagenet-mlx 0.2.1
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Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
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| radimagenet_mlx-0.2.1-py3-none-any.whl | Python 3 | none | any | Details for radimagenet_mlx-0.2.1-py3-none-any.whl |
Total release size: 50.5 kB
Release files / radimagenet_mlx-0.2.1.tar.gz
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