RetinaNet · Lung Nodules

Detect lung nodule candidates in 3D chest CT with the MONAI RetinaNet checkpoint.

Input	Output
3D chest CT · NIfTI	Candidate boxes and scores

Apple Silicon · Metal · Python 3.13

Quick start

uv venv --python 3.13
source .venv/bin/activate

uv pip install "lung-nodule-mlx>=0.2.6"
uv tool install huggingface-hub

hf download MedMLX/retinanet-lung-nodule-mlx --revision d2d635fbac34254f14192ac348a05d275a949317 --local-dir models

Replace the example input paths with your own files.

from lung_nodule_mlx.medmlx import run

result = run(
    image_path='ct.nii.gz',
    weights_path='models/network.safetensors',
    output_dir='outputs',
    resample_raw_input=True,
)
print(result["outputs"])

Outputs are candidates requiring review.

Use & license

For research and education. Not validated for clinical use.

Weights: Apache-2.0. Notices.

Paper references

RetinaNet architecture: Focal Loss for Dense Object Detection.

Citation

If you use MedMLX, please cite the MedMLX paper:

@article{sandoval2026medmlx,
  title   = {MedMLX: On-Device Medical Imaging AI for Apple Silicon},
  author  = {Sandoval, J. and Sandoval, A. and Saleem, A.},
  journal = {medRxiv},
  year    = {2026},
  doi     = {10.64898/2026.10.28.26366762}
}