Update model card with Primus paper link and segmentation 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
+# Primus: Enforcing Attention Usage for 3D Medical Image Segmentation
+
+This repository hosts pre-trained checkpoints from the **OpenMind** benchmark, specifically for the **Primus-M** transformer-based encoder.
 
-> **Model from the 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)
+- **Paper**: [Primus: Enforcing Attention Usage for 3D Medical Image Segmentation](https://huggingface.co/papers/2503.01835)
+- **SSL Benchmark Paper**: [An OpenMind for 3D medical vision self-supervised learning](https://huggingface.co/papers/2412.17041)
+- **Pre-training Codebase**: [MIC-DKFZ/nnssl](https://github.com/MIC-DKFZ/nnssl)
+- **Dataset**: [AnonRes/OpenMind](https://huggingface.co/datasets/AnonRes/OpenMind)
+- **Downstream Fine-tuning Framework**: [nnU-Net](https://github.com/MIC-DKFZ/nnUNet)
 
 ---
 
@@ -20,33 +23,50 @@ 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.
+Primus and PrimusV2 are Transformer-centric segmentation architectures designed for 3D medical imaging. While traditional hybrid models often over-rely on convolutional blocks, Primus leverages high-resolution tokens and advanced block design to maximize the effectiveness of its Transformer layers. It achieves state-of-the-art performance across multiple public datasets, competing with or exceeding standard CNN-based models like ResEnc-L and MedNeXt.
 
-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 repository was pre-trained using a self-supervised learning (SSL) method on the [OpenMind Dataset](https://huggingface.co/datasets/AnonRes/OpenMind), a 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).
-*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.*
-
----
+**Note**: These models are intended to be used as backbones for downstream adaptation. We recommend using the [nnU-Net](https://github.com/MIC-DKFZ/nnUNet) framework for fine-tuning on segmentation and classification tasks.
 
 ## Model Variants
 
-We release SSL checkpoints for two backbone architectures:
+We release SSL checkpoints for the following transformer-based backbone:
+
+- **Primus-M**: A transformer-centric encoder introduced in the Primus paper.
+
+Pre-training techniques included in the benchmark:
+- **Models Genesis (MG)**: Reconstruction and denoising based pre-training.
+- **Masked Autoencoders (MAE)**: Reconstruction based pre-training.
+- **SimMIM**: Simple masked reconstruction based pre-training.
+- **Volume Contrastive (VoCo)**, **VolumeFusion (VF)**, **SimCLR**, and more.
+
+## Usage
+
+The models are integrated into the `nnU-Net` framework. You can install it via pip:
+
+```bash
+pip install nnunetv2
+```
+
+For detailed instructions on training and inference, please refer to the [nnU-Net Primus documentation](https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/primus.md).
+
+## Citation
 
-- **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)]
+If you use this model, please cite the following papers:
 
-Each encoder has been pre-trained using one of the following SSL techniques:
+```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}
+}
 
-| 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 |
+@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}
+}
+```