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] [arXiv:2503.01835]
These weights were also utilized in the OpenMind benchmark study:
📄 An OpenMind for 3D medical vision self-supervised learning
[arXiv: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, 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 and the main nnU-Net framework.
Model Variants
We release SSL checkpoints for two backbone architectures:
- ResEnc-L: A CNN-based encoder [a, b]
- Primus-M: A transformer-based encoder [Primus paper]
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
- Pre-training codebase: MIC-DKFZ/nnssl
Citation
If you use this model, please cite the following:
@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}
}