Add model card for Light-UNETR

README.md

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+---
+license: mit
+pipeline_tag: image-segmentation
+tags:
+- medical
+- 3d
+- transformer
+---
+
+# Light-UNETR
+
+Light-UNETR is a lightweight transformer architecture designed for efficient 3D medical image segmentation, introduced in the paper [Harnessing Lightweight Transformer with Contextual Synergic Enhancement for Efficient 3D Medical Image Segmentation](https://huggingface.co/papers/2603.23390).
+
+The model addresses computational efficiency through a Lightweight Dimension Reductive Attention (LIDR) module and a Compact Gated Linear Unit (CGLU). To improve data efficiency, the authors propose a Contextual Synergic Enhancement (CSE) learning strategy.
+
+## Resources
+
+- **Paper:** [Harnessing Lightweight Transformer with Contextual Synergic Enhancement for Efficient 3D Medical Image Segmentation](https://huggingface.co/papers/2603.23390)
+- **Code:** [Official GitHub Repository](https://github.com/CUHK-AIM-Group/Light-UNETR)
+
+## Performance
+
+Light-UNETR significantly reduces computational costs compared to standard architectures. For instance, on the Left Atrial (LA) Segmentation dataset, it reduces FLOPs by 90.8% and parameters by 85.8% compared to state-of-the-art methods while achieving superior performance even with limited (10%) labeled data.
+
+## Sample Usage
+
+To test a pre-trained Light-UNETR model using the official implementation, you can use the following command structure:
+
+```bash
+# Example: Test BraTS model
+python test_cse.py --dataset brats --model lightunetr --checkpoint lightunetr_best_model_brats_25lab.pth --gpu 0
+```
+
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@article{liu2025harnessing,
+  title={Harnessing Lightweight Transformer with Contextual Synergic Enhancement for Efficient 3D Medical Image Segmentation},
+  author={Liu, Xinyu and Chen, Zhen and Li, Wuyang and Li, Chenxin and Yuan, Yixuan},
+  year={2025}
+}
+```
+
+## Acknowledgement
+
+The authors appreciate the contributions of [SSL4MIS](https://github.com/HiLab-git/SSL4MIS), [Slim UNETR](https://github.com/aigzhusmart/Slim-UNETR), [BCP](https://github.com/DeepMed-Lab-ECNU/BCP), and other referenced codebases.