| --- |
| tags: |
| - medical |
| license: other |
| license_name: research-only-rail-m |
| model-index: |
| - name: Curia-MAE |
| results: [] |
| extra_gated_prompt: >- |
| Please confirm that you have read and agree to the following disclaimer. |
| |
| The model in this repository is provided for |
| research use only (Research-only RAIL-M license). |
| The model(s) and/or software are not |
| intended for use in clinical decision-making or for any other clinical use, |
| and performance for clinical use has not been established. |
|
|
| --- |
| |
| <div align="center"> |
| <img src="https://cdn-uploads.huggingface.co/production/uploads/62cdea59a9be5c195561c2b8/lgz6FefJZr9nMkqQ4_Y5T.png" width="40%" alt="Raidium" /> |
| </div> |
| <hr> |
|
|
|
|
| <p align="center"> |
| <a href="https://www.raidium.eu/"><b>🌐 Raidium</b></a> | |
| <a href="https://arxiv.org/abs/2608.05844"><b>📄 Paper Link</b></a> | |
| <a href="https://huggingface.co/raidium/curia-2"><b>🤗 Curia-2</b></a> | |
| <a href="https://huggingface.co/raidium/curia"><b>🤗 Curia</b></a> |
| </p> |
| <h2> |
| <p align="center"> |
| <h1 align="center">Curia-MAE: Multi-Modal Multi-Anatomy MAE Pre-Training for 3D Medical Image Segmentation</h1> |
| </p> |
| </h2> |
|
|
|
|
| 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](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](https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/finetuning_from_nnssl_checkpoints.md): |
|
|
| ```bash |
| # 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](https://github.com/MIC-DKFZ/nnUNet) (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. |
|
|
