Instructions to use MedMLX/segresnet-wholebody-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use MedMLX/segresnet-wholebody-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download MedMLX/segresnet-wholebody-mlx --local-dir segresnet-wholebody-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
SegResNet · Whole-body CT
Native MLX weights for MONAI's 104-structure whole-body CT segmentation models. The high-resolution and low-resolution checkpoints correspond to 1.5 mm and 3 mm inference respectively.
| Bundle | Weights |
|---|---|
wholeBody_ct_segmentation |
segresnet-wholebody-high.safetensors |
wholeBody_ct_segmentation_lowres |
segresnet-wholebody-low.safetensors |
The source is MONAI/wholeBody_ct_segmentation at revision 13f6d33ae9de6664571d935ee647039f477c3fad, files models/model.pt and models/model_lowres.pt. The float32 tensors retain their source names and layouts in safetensors. Model architecture and inference are provided by segresnet-mlx.
The models were trained on TotalSegmentator data; see the upstream model card and data attribution. The original MONAI license is included as LICENSE. These weights are converted copies of the source checkpoints.
For research and education. Not validated for clinical use; outputs require human review.
Citation
If you use MedMLX, please cite the MedMLX paper:
@article{sandoval2026medmlx,
title = {MedMLX: On-Device Medical Imaging AI for Apple Silicon},
author = {Sandoval, A. and Sandoval, J. and Saleem, A.},
journal = {medRxiv},
year = {2026},
doi = {10.64898/2026.10.28.26366762}
}
Quantized
Model tree for MedMLX/segresnet-wholebody-mlx
Base model
MONAI/wholeBody_ct_segmentation