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
source .venv/bin/activate
uv pip install radimagenet-mlx
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 .h5 weights load directly; TensorFlow is not needed. 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 0.2.2
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| radimagenet_mlx-0.2.2-py3-none-any.whl | Python 3 | none | any | Details for radimagenet_mlx-0.2.2-py3-none-any.whl |
Total release size: 50.9 kB
Release files / radimagenet_mlx-0.2.2.tar.gz
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