Link to Primus paper and update pipeline tag

README.md

@@ -1,12 +1,22 @@
 ---
-license: cc-by-4.0
 datasets:
 - AnonRes/OpenMind
-pipeline_tag: image-feature-extraction
+license: cc-by-4.0
+pipeline_tag: image-segmentation
 tags:
 - medical
 ---
 
+# Primus: Enforcing Attention Usage for 3D Medical Image Segmentation
+
+This repository contains pre-trained checkpoints for **Primus**, a Transformer-centric architecture for 3D medical image segmentation.
+
+- **Paper**: [Primus: Enforcing Attention Usage for 3D Medical Image Segmentation](https://huggingface.co/papers/2503.01835)
+- **Code**: [MIC-DKFZ/nnUNet](https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/primus.md)
+- **Pre-training codebase**: [MIC-DKFZ/nnssl](https://github.com/MIC-DKFZ/nnssl)
+
+---
+
 # OpenMind Benchmark 3D SSL Models
 
 > **Model from the paper**: [An OpenMind for 3D medical vision self-supervised learning](https://arxiv.org/abs/2412.17041)  
@@ -36,7 +46,7 @@ Each model was pre-trained using a particular SSL method on the [OpenMind Datase
 We release SSL checkpoints for two backbone architectures:
 
 - **ResEnc-L**: A CNN-based encoder [[a](https://arxiv.org/abs/2410.23132), [b](https://arxiv.org/abs/2404.09556)]  
-- **Primus-M**: A transformer-based encoder [[Primus paper](https://arxiv.org/abs/2503.01835)]
+- **Primus-M**: A transformer-based encoder introduced in the [Primus paper](https://huggingface.co/papers/2503.01835)
 
 Each encoder has been pre-trained using one of the following SSL techniques:
 
@@ -50,3 +60,23 @@ Each encoder has been pre-trained using one of the following SSL techniques:
 | [SimMIM](https://openaccess.thecvf.com/content/CVPR2022/html/Xie_SimMIM_A_Simple_Framework_for_Masked_Image_Modeling_CVPR_2022_paper.html) | Simple masked reconstruction based pretraining method (TR only) |
 | [SwinUNETR SSL](https://arxiv.org/abs/2111.14791) | Rotation, Contrastive and Reconstruction based pre-training method. |
 | [SimCLR](https://arxiv.org/abs/2002.05709) | Transfer of 2D Contrastive learning baseline method to 3D |
+
+## Citation
+
+If you find this model or the OpenMind benchmark useful, please cite:
+
+```text
+@article{wald2025primus,
+  title={Primus: Enforcing Attention Usage for 3D Medical Image Segmentation},
+  author={Wald, Tassilo and Roy, Saikat and Isensee, Fabian and Ulrich, Constantin and Ziegler, Sebastian and Trofimova, Dasha and Stock, Raphael and Baumgartner, Michael and K{\"o}hler, Gregor and Maier-Hein, Klaus},
+  journal={arXiv preprint arXiv:2503.01835},
+  year={2025}
+}
+
+@article{wald2024openmind,
+  title={An OpenMind for 3D medical vision self-supervised learning},
+  author={Wald, Tassilo and Ulrich, Constantin and Suprijadi, J. and Ziegler, Sebastian and Nohel, M. and Peretzke, R. and others},
+  journal={arXiv preprint arXiv:2412.17041},
+  year={2024}
+}
+```