Create README.md

README.md

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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}, 
+}
+```