| --- |
| datasets: |
| - AnonRes/OpenMind |
| license: cc-by-4.0 |
| pipeline_tag: image-segmentation |
| tags: |
| - medical |
| --- |
| |
| # Primus-M (SimCLR Pre-trained) — OpenMind Benchmark |
|
|
| > **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) |
|
|
| --- |
|
|
|  |
|
|
| ## Overview |
|
|
| 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. |
|
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| 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). |
|
|
| *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 |
|
|
| 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 introduced in the [Primus paper](https://huggingface.co/papers/2503.01835). |
|
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| 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} |
| } |
| ``` |
