lesiondiffusion-mlx

Add synthetic lesions to CT with native MLX, with controls for organ, size, placement and phenotype.

Docs · MedMLX

Liver CT before and after lesion generation; the contour marks the generated lesion.

Apple Silicon · Metal · Python 3.13 / 3.14 · uv

Quick start

uv venv --python 3.13
source .venv/bin/activate
repo="https://github.com/MedMLX/lesiondiffusion-mlx"
uv pip install \
  "lesiondiffusion-mlx[conversion] @ git+$repo" \
  huggingface-hub

Get LMNet.ckpt, LINet.ckpt and VQ.ckpt from the upstream weights folder. Convert once, then generate:

from pathlib import Path

from huggingface_hub import hf_hub_download
from lesiondiffusion_mlx import write_conversion

tokenizer = hf_hub_download("microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224", "tokenizer.json")
write_conversion(
    checkpoints={"lmnet": Path("LMNet.ckpt"), "linet": Path("LINet.ckpt"), "vq": Path("VQ.ckpt")},
    tokenizer_path=Path(tokenizer),
    output_dir=Path("lesiondiffusion-weights"),
)

from lesiondiffusion_mlx.medmlx import run

result = run(
    image_path="ct_1mm_ras.nii.gz",
    organ_mask_path="liver_mask.nii.gz",
    weights_path="lesiondiffusion-weights",
    output_dir="out",
    organ="liver",
    lesion_type="tumor",
    bbox_size_mm=[30, 30, 20],
    placement="inside_parenchyma",
)
print(result["outputs"]["synthetic_image"])

Inputs must support the documented crop sizes. Outputs use a RAS 1 mm grid; voxels outside the lesion can change. Synthetic output is not ground truth.

License

Apache-2.0. See third-party notices. Model weights retain their publisher terms.

For research and education. Outputs require human review.