AnonRes/ResEncL-OpenMind-SwinUNETR

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

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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.

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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:

Method	Description
Volume Contrastive (VoCo)	Contrastive pretraining method for 3D volumes
VolumeFusion (VF)	Spatial volume fusion-based segmentation SSL method
Models Genesis (MG)	Reconstruction and denoising based pretraining method
Masked Autoencoders (MAE)	Default reconstruction based pretraining method
Spark 3D (S3D)	Sparse reconstruction based pretraining mehtod (CNN only)
SimMIM	Simple masked reconstruction based pretraining method (TR only)
SwinUNETR SSL	Rotation, Contrastive and Reconstruction based pre-training method.
SimCLR	Transfer of 2D Contrastive learning baseline method to 3D