Curia-2: Scaling Self-Supervised Learning for Radiology Foundation Models
Paper • 2604.01987 • Published
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🌐 Blog Post | 📄 Curia-2 Paper Link | 🤗 Original Curia | 📄 Original Curia Paper Link
Curia-2: Scaling Self-Supervised Learning for Radiology Foundation Models
We introduce Curia-2, a follow-up to Curia which significantly improves the original pre-training strategy and representation quality to better capture the specificities of radiological data. Curia-2 excels on vision-focused tasks and fairs competitively to vision-language models on clinically complex tasks such as finding detection.
Loading the model
To load the model, use the AutoModel class from huggingface transformers library.
from transformers import AutoModel
model = AutoModel.from_pretrained("raidium/curia-2")
You can also load the image pre-processor
from transformers import AutoImageProcessor
processor = AutoImageProcessor.from_pretrained("raidium/curia-2", trust_remote_code=True)
Then to forward an image:
img = 2048 * np.random.rand(256, 256) - 1024 # single axial slice, in PL orientation
model_input = processor(img)
features = model(**model_input)
The image must follow the following format:
input: numpy array of shape (H, W)
Images needs to be in:
- PL for axial
- IL for coronal
- IP for sagittal
for CT, no windowing, just hounsfield or normalized image
for MRI, similar, no windowing, just raw values or normalized image
License
The model is released under the RESEARCH-ONLY RAIL-M license. https://huggingface.co/raidium/curia/blob/main/LICENSE
Cite our paper
@article{saporta2026curia2,
title={Curia-2: Scaling Self-Supervised Learning for Radiology Foundation Models},
author={Antoine Saporta and Baptiste Callard and Corentin Dancette and Julien Khlaut and Charles Corbière and Leo Butsanets and Amaury Prat and Pierre Manceron},
year={2026},
eprint={2604.01987},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2604.01987},
}
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