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:
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