---
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}, 
}
```