OpenMind Benchmark 3D SSL Models

Model from the 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 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) — 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.

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. 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, b]
• Primus-M: A transformer-based encoder [Primus paper]

Each encoder has been pre-trained using one of the following SSL techniques: