| --- |
| datasets: |
| - AnonRes/OpenMind |
| license: cc-by-4.0 |
| pipeline_tag: image-segmentation |
| tags: |
| - medical |
| --- |
| |
| # OpenMind Benchmark 3D SSL Models: Primus-M |
|
|
| This repository hosts pre-trained checkpoints for the **Primus-M** backbone architecture, as introduced in the paper: |
|
|
| 📄 **Primus: Enforcing Attention Usage for 3D Medical Image Segmentation** |
| (Wald, T., Roy, S., Isensee, F., Ulrich, C., Ziegler, S., Trofimova, D., Stock, R., Baumgartner, M., Köhler, G., & Maier-Hein, K.) |
| [[Hugging Face Paper](https://huggingface.co/papers/2503.01835)] [[arXiv:2503.01835](https://arxiv.org/abs/2503.01835)] |
|
|
| These weights were also utilized in the **OpenMind** benchmark study: |
| 📄 **An OpenMind for 3D medical vision self-supervised learning** |
| [[arXiv:2412.17041](https://arxiv.org/abs/2412.17041)] |
|
|
| --- |
|
|
|  |
|
|
| ## Overview |
|
|
| This repository hosts pre-trained checkpoints from the **OpenMind** benchmark, the first extensive benchmark study for **self-supervised learning (SSL)** on **3D medical imaging** data. |
|
|
| 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. |
|
|
| This specific checkpoint uses the **Primus-M** transformer-centric encoder pre-trained with the **VolumeFusion (VF)** self-supervised method. Primus addresses the over-reliance on convolutional blocks in existing architectures, leveraging high-resolution tokens and advanced block design to achieve state-of-the-art performance in 3D medical image segmentation. |
|
|
| **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) and the main [nnU-Net](https://github.com/MIC-DKFZ/nnUNet) framework. |
|
|
| ## Model Variants |
|
|
| 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)] |
|
|
| Each encoder has been pre-trained using one of the following SSL techniques: Volume Contrastive (VoCo), VolumeFusion (VF), Models Genesis (MG), Masked Autoencoders (MAE), Spark 3D (S3D), SimMIM, SwinUNETR SSL, or SimCLR. |
|
|
| ## Implementation |
|
|
| The code for Primus and the fine-tuning pipeline is available in the official nnU-Net repository: |
| - **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) |
|
|
| ## Citation |
|
|
| If you use this model, please cite the following: |
|
|
| ```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} |
| } |
| ``` |
