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---
license: mit
datasets:
- mcosarinsky/CheXmask-U
tags:
- medical
---
# CheXmask-U: Uncertainty-aware landmark-based anatomical segmentation for chest X-rays
📄 [Paper](https://arxiv.org/abs/2512.10715) | 💻 [Code](https://github.com/mcosarinsky/CheXmask-U) | 🎛️ [Interactive Demo](https://huggingface.co/spaces/mcosarinsky/CheXmask-U) | 📦 [Dataset](https://huggingface.co/datasets/mcosarinsky/CheXmask-U)
---
## Model Description
CheXmask-U is a **landmark-based chest X-ray segmentation model** providing node-wise **uncertainty estimation**. It outputs:
- Anatomical landmarks for lung and heart structures
- Latent uncertainty from the learned variational latent space
- Predictive uncertainty via stochastic output sampling
The model is implemented using a hybrid graph-convolutional architecture (`HybridGNet`), combining convolutional encoders with graph-based decoders. For full usage and code, see the [GitHub repository](https://github.com/mcosarinsky/CheXmask-U).
---
## Usage
```python
from models.HybridGNet2IGSC import HybridGNetHF
device = "cuda" # or "cpu"
model = HybridGNetHF.from_pretrained(
"mcosarinsky/CheXmask-U",
subfolder="v1_skip",
device=device
)
# xray_image: tensor or suitable input
landmarks, _, _ = model(xray_image)
```
## Citation
If you use this model, please cite:
```bibtex
@misc{cosarinsky2025chexmaskuquantifyinguncertaintylandmarkbased,
title={CheXmask-U: Quantifying uncertainty in landmark-based anatomical segmentation for X-ray images},
author={Matias Cosarinsky and Nicolas Gaggion and Rodrigo Echeveste and Enzo Ferrante},
year={2025},
eprint={2512.10715},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2512.10715},
}
``` |