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
Benchmark paper: An OpenMind for 3D medical vision self-supervised learning
Pre-training codebase used to create checkpoint: MIC-DKFZ/nnssl
Dataset: AnonRes/OpenMind
Downstream (segmentation) fine-tuning: 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.
Each model in this benchmark was pre-trained using a particular self-supervised learning (SSL) method on the OpenMind Dataset, 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.
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, b]
- Primus-M: A transformer-based encoder introduced in the Primus paper.
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:
@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}
}