Add HiLo model card and checkpoints

.gitignore

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+.DS_Store

CAS2023_checkpoint_best.pth

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ImageCAS_checkpoint_best.pth

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README.md

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+---
+license: mit
+library_name: pytorch
+pipeline_tag: image-segmentation
+tags:
+- medical-image-segmentation
+- 3d-segmentation
+- vessel-segmentation
+- nnunet
+- pytorch
+---
+
+# HiLo: Spatial-Spectral Hybrid High-Low Frequency Activation for Heart and Brain Vessel Segmentation
+
+HiLo is a 3D vessel segmentation framework for cardio-cerebrovascular imaging. It jointly models spatial and spectral high-/low-frequency cues to preserve fine tubular details while encoding large-scale anatomical structure.
+
+> This work is published in [Expert Systems with Applications](https://www.sciencedirect.com/science/article/pii/S0957417426023626).
+
+[![github](https://img.shields.io/badge/GitHub-deepang--ai/HiLo-blue)](https://github.com/deepang-ai/HiLo) 
+[![huggingface weights](https://img.shields.io/badge/%F0%9F%A4%97%20Weights-deepang/HiLo-yellow)](https://huggingface.co/deepang/HiLo) 
+
+## Weights
+
+- [ImageCAS checkpoint](https://huggingface.co/deepang/HiLo/blob/main/ImageCAS_checkpoint_best.pth)
+- [CAS2023 checkpoint](https://huggingface.co/deepang/HiLo/blob/main/CAS2023_checkpoint_best.pth)
+
+## Usage
+
+Please use these weights with the official code repository:
+
+```bash
+git clone https://github.com/deepang-ai/HiLo.git
+cd HiLo
+```
+
+Follow the installation, data preparation, training, evaluation, and inference instructions in the [HiLo README](https://github.com/deepang-ai/HiLo#readme). HiLo depends on the bundled project-adapted `nnUNet/` package in the code repository, so install it with:
+
+```bash
+pip install -e ./nnUNet
+```
+
+## Citation
+
+If you use HiLo or these pretrained weights, please cite:
+
+```bibtex
+@article{HUANG2026133453,
+title = {HiLo: Spatial-Spectral Hybrid High-Low Frequency Activation for Heart and Brain Vessel Segmentation},
+journal = {Expert Systems with Applications},
+pages = {133453},
+year = {2026},
+issn = {0957-4174},
+doi = {https://doi.org/10.1016/j.eswa.2026.133453},
+url = {https://www.sciencedirect.com/science/article/pii/S0957417426023626},
+author = {Jiahui Huang and Xin Lei and Qiong Wang and Valentin Sinitsyn and Yun Zhu and Ying Hu and Hao Chen and Yan Pang}
+}
+```
+
+Please also cite nnU-Net when using the bundled training framework:
+
+```bibtex
+@article{isensee2021nnunet,
+  title={nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
+  author={Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
+  journal={Nature Methods},
+  volume={18},
+  number={2},
+  pages={203--211},
+  year={2021},
+  publisher={Nature Publishing Group}
+}
+```