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🌐 Raidium | 📄 Paper Link | 🤗 Curia-2 | 🤗 Curia

Curia-MAE: Multi-Modal Multi-Anatomy MAE Pre-Training for 3D Medical Image Segmentation

We introduce Curia-MAE, a multi-modal, multi-anatomy Masked Autoencoder (MAE) pre-trained on roughly 300,000 CT and MRI volumes covering a large number of anatomical sites. Curia-MAE targets dense 3D medical image segmentation: it provides a strong, reusable frozen encoder that can be adapted to a new segmentation task by training only a lightweight decoder on top, while remaining competitive under full fine-tuning and particularly strong on lesion-focused tasks where labeled data is scarce. A single frozen encoder can thus be reused across diverse segmentation tasks, reducing the cost of adapting and deploying such models in clinical workflows.

Check the research paper: https://arxiv.org/abs/2608.05844

Finetuning with nnU-Net

Curia-MAE is a ResEnc-L (residual-encoder U-Net) backbone in the nnU-Net self-supervised pre-training format. Finetune it on a labeled 3D segmentation dataset with nnU-Net's finetuning-from-nnssl workflow:

# 1) dataset fingerprint + base plan
nnUNetv2_extract_fingerprint -d <DATASET_ID>
nnUNetv2_plan_experiment    -d <DATASET_ID>

# 2) preprocess to the pre-training spacing/normalization and register this checkpoint
nnUNetv2_preprocess_like_nnssl -d <DATASET_ID> -n Curia-MAE \
    -pc checkpoint_final.pth -am like_pretrained

# 3) finetune (the generated plan already embeds the checkpoint path)
nnUNetv2_train_pretrained <DATASET_ID> 3d_fullres <FOLD> \
    -p ptPlans__Curia-MAE____Spacing__1.00_1.00_1.00___Norm__Z -tr PretrainedTrainer

Only the encoder and stem are transferred; the segmentation decoder is trained from scratch. The architecture and preprocessing plan are also provided in adaptation_plan.json.

License

The model is released under the RESEARCH-ONLY RAIL-M license. https://huggingface.co/raidium/curia/blob/main/LICENSE

Cite our paper

@article{danielou2026curiamae,
      title={Curia-MAE: Multi-Modal Multi-Anatomy MAE Pre-Training for 3D Medical Image Segmentation}, 
      author={Théo Danielou and Antoine Saporta and Léo Alberge and Corentin Dancette},
      year={2026},
      eprint={2608.05844},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2608.05844}, 
}

Acknowledgements

Curia-MAE uses the ResEnc-L residual-encoder U-Net architecture from nnU-Net v2 (Division of Medical Image Computing, German Cancer Research Center / DKFZ), and the released checkpoint is packaged to be fine-tunable within the nnU-Net framework. We gratefully acknowledge the nnU-Net project.

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Paper for raidium/Curia-MAE

Curia-MAE: Multi-Modal Multi-Anatomy MAE Pre-Training for 3D Medical Image Segmentation

Paper • 2608.05844 • Published 28 days ago • 1