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-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)
-
----
-
-![OpenMind](https://huggingface.co/datasets/AnonRes/OpenMind/resolve/main/assets/OpenMindDataset.png)
-
-## 🔍 Overview
-
-This repository hosts pre-trained checkpoints from the **OpenMind** benchmark:  
-📄 **"An OpenMind for 3D medical vision self-supervised learning"**  
-([arXiv:2412.17041](https://arxiv.org/abs/2412.17041)) — the first extensive benchmark study for **self-supervised learning (SSL)** on **3D medical imaging** data.
-
-The models were pre-trained using various SSL methods 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.** Instead we recommend using the downstream fine-tuning pipelines for **segmentation** and **classification**, available in the [adaptation repository](https://github.com/TaWald/nnUNet).
-*While direct download is possible, we recommend using the auto-download of the respective fine-tuning repositories.*
-
----
-
-## 🧠 Model Variants
-
-We release SSL checkpoints for two backbone architectures:
-
-- **ResEnc-L**: A CNN-based encoder [[link1](https://arxiv.org/abs/2410.23132), [link2](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 the following SSL techniques:
-
-| Method        | Description |
-|---------------|-------------|
-| [Volume Contrastive (VoCo)](https://arxiv.org/abs/2402.17300) | Global contrastive learning in 3D volumes |
-| [VolumeFusion (VF)](https://arxiv.org/abs/2306.16925)         | Spatial fusion-based SSL |
-| [Models Genesis (MG)](https://www.sciencedirect.com/science/article/pii/S1361841520302048) | Classic 3D self-reconstruction |
-| [Masked Autoencoders (MAE)](https://openaccess.thecvf.com/content/CVPR2022/html/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper) | Patch masking and reconstruction |
-| [Spark 3D (S3D)](https://arxiv.org/abs/2410.23132) | 3D adaptation of Spark framework |
-| [SimMIM](https://openaccess.thecvf.com/content/CVPR2022/html/Xie_SimMIM_A_Simple_Framework_for_Masked_Image_Modeling_CVPR_2022_paper.html) | Simple masked reconstruction |
-| [SwinUNETR SSL](https://arxiv.org/abs/2111.14791) | Transformer-based pre-training |
-| [SimCLR](https://arxiv.org/abs/2002.05709) | Contrastive learning baseline |