PrimusM OpenMind SimCLR

--- license: cc-by-4.0 datasets: - AnonRes/OpenMind pipeline_tag: image-feature-extraction 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) > **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 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. 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). *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: - **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...