Update pipeline tag and add Primus paper information

README.md

@@ -1,15 +1,16 @@
 ---
-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
+# Primus-M (SimCLR Pre-trained) — OpenMind Benchmark
 
-> **Model from the paper**: [An OpenMind for 3D medical vision self-supervised learning](https://arxiv.org/abs/2412.17041)  
+> **Model from the paper**: [Primus: Enforcing Attention Usage for 3D Medical Image Segmentation](https://huggingface.co/papers/2503.01835)  
+> **Benchmark paper**: [An OpenMind for 3D medical vision self-supervised learning](https://arxiv.org/abs/2412.17041)  
 > **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)
@@ -20,33 +21,41 @@ tags:
 
 ## Overview
 
-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.
+This repository hosts a pre-trained checkpoint from the **OpenMind** benchmark using the **Primus-M** backbone. Primus is a Transformer-centric segmentation architecture designed specifically to maximize the effectiveness of attention mechanisms in 3D medical imaging, surpassing hybrid architectures and competing with state-of-the-art CNNs.
 
-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.
+Each model in this benchmark was pre-trained using a particular self-supervised learning (SSL) method on the [OpenMind Dataset](https://huggingface.co/datasets/AnonRes/OpenMind), a large-scale, standardized collection of public brain MRI datasets.
+
+**Note**: These models are primarily intended for downstream fine-tuning. We recommend using the adaptation frameworks for segmentation and classification provided in the [nnU-Net adaptation repository](https://github.com/TaWald/nnUNet).
 
-**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).
 *While manual download is possible, we recommend using the auto-download feature of the fine-tuning repository by providing the repository URL on Hugging Face instead of a local checkpoint path.*
 
 ---
 
 ## Model Variants
 
-We release SSL checkpoints for two backbone architectures:
+In the OpenMind benchmark, SSL checkpoints are released 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)]
-
-Each encoder has been pre-trained using one of the following SSL techniques:
-
-| Method        | Description |
-|---------------|-------------|
-| [Volume Contrastive (VoCo)](https://arxiv.org/abs/2402.17300) | Contrastive pretraining method for 3D volumes |
-| [VolumeFusion (VF)](https://arxiv.org/abs/2306.16925)         | Spatial volume fusion-based segmentation SSL method |
-| [Models Genesis (MG)](https://www.sciencedirect.com/science/article/pii/S1361841520302048) | Reconstruction and denoising based pretraining method |
-| [Masked Autoencoders (MAE)](https://openaccess.thecvf.com/content/CVPR2022/html/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper) | Default reconstruction based pretraining method |
-| [Spark 3D (S3D)](https://arxiv.org/abs/2410.23132) | Sparse reconstruction based pretraining mehtod (CNN only) |
-| [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 |
+- **Primus-M**: A transformer-based encoder introduced in the [Primus paper](https://huggingface.co/papers/2503.01835).
+
+Each encoder has been pre-trained using various SSL techniques such as SimCLR, Volume Contrastive (VoCo), VolumeFusion (VF), and Masked Autoencoders (MAE). This specific checkpoint uses **SimCLR**.
+
+## Citation
+
+If you use this model, 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}
+}
+```