Update model card: add Primus paper link and update pipeline tag

README.md

@@ -1,18 +1,21 @@
 ---
-license: cc-by-4.0
 datasets:
 - AnonRes/OpenMind
-pipeline_tag: image-feature-extraction
+license: cc-by-4.0
+pipeline_tag: image-segmentation
 tags:
 - medical
 ---
 
 # OpenMind Benchmark 3D SSL Models
 
-> **Model from the paper**: [An OpenMind for 3D medical vision self-supervised learning](https://arxiv.org/abs/2412.17041)  
+> **Models from the papers**: 
+> - [An OpenMind for 3D medical vision self-supervised learning](https://arxiv.org/abs/2412.17041)
+> - [Primus: Enforcing Attention Usage for 3D Medical Image Segmentation](https://huggingface.co/papers/2503.01835)
 > **Pre-training codebase used to create checkpoint**: [MIC-DKFZ/nnssl](https://github.com/MIC-DKFZ/nnssl)  
 > **Dataset**: [AnonRes/OpenMind](https://huggingface.co/datasets/AnonRes/OpenMind)  
 > **Downstream (segmentation) fine-tuning**: [TaWald/nnUNet](https://github.com/TaWald/nnUNet)
+> **Official Code Documentation**: [Primus in nnU-Net](https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/primus.md)
 
 ---
 
@@ -24,6 +27,10 @@ This repository hosts pre-trained checkpoints from the **OpenMind** benchmark:
 📄 **An OpenMind for 3D medical vision self-supervised learning** (Wald, T., Ulrich, C., Suprijadi, J., Ziegler, S., Nohel, M., Peretzke, R., ... & Maier-Hein, K. H. (2024).)  
 ([arXiv:2412.17041](https://arxiv.org/abs/2412.17041)) — the first extensive benchmark study for **self-supervised learning (SSL)** on **3D medical imaging** data.
 
+It also features the **Primus** architecture:
+📄 **Primus: Enforcing Attention Usage for 3D Medical Image Segmentation** (Wald, T., Roy, S., Isensee, F., Ulrich, C., Ziegler, S., Trofimova, D., ... & Maier-Hein, K. H. (2025).)  
+([Hugging Face Papers](https://huggingface.co/papers/2503.01835)) — introduction of Transformer-centric segmentation architectures that achieve state-of-the-art results.
+
 Each model was pre-trained using a particular SSL method on the [OpenMind Dataset](https://huggingface.co/datasets/AnonRes/OpenMind), a large-scale, standardized collection of public brain MRI datasets.
 
 **These models are not recommended to be used as-is for feature extraction.** Instead we recommend using the downstream fine-tuning frameworks for **segmentation** and **classification** adaptation, available in the [adaptation repository](https://github.com/TaWald/nnUNet).
@@ -36,7 +43,7 @@ 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 [[Primus paper](https://huggingface.co/papers/2503.01835)]
 
 Each encoder has been pre-trained using one of the following SSL techniques:
 
@@ -50,3 +57,23 @@ 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 use these models or the Primus architecture, please cite:
+
+```bibtex
+@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 Köhler, 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}
+}
+```