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🌟 Github | 📄 Paper Link | 🌐 Blog post

Curia: A Multi-Modal Foundation Model for Radiology

We introduce Curia, a foundation model trained on the entire cross-sectional imaging output of a major hospital over several years—which to our knowledge is the largest such corpus of real-world data—encompassing 150,000 exams (130 TB). On a newly curated 19-task external validation benchmark, Curia accurately identifies organs, detects conditions like brain hemorrhages and myocardial infarctions, and predicts outcomes in tumor staging. Curia meets or surpasses the performance of radiologists and recent foundation models, and exhibits clinically significant emergent properties in cross-modality, and low-data regimes.

Check the research paper: https://arxiv.org/abs/2509.06830

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")

You can also load the image pre-processor

from transformers import AutoImageProcessor
processor = AutoImageProcessor.from_pretrained("raidium/curia", trust_remote_code=True)

Then to forward an image:

img = np.random.rand(-1024, 1024, size=(256, 256)) # 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

Loading model with heads

The following heads are available:

anatomy-ct
anatomy-mri
atlas-stroke
covidx-ct
deep-lesion-site
emidec-classification-mask
ich
ixi
kits
kneeMRI
luna16-3D
neural_foraminal_narrowing
oasis
spinal_canal_stenosis
subarticular_stenosis

To load the head, specify its name when loading the model

from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("raidium/curia", trust_remote_code=True)

model = AutoModelForImageClassification.from_pretrained(
    "raidium/curia", subfolder="anatomy-ct", trust_remote_code=True
)

License

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

Cite our paper

@article{dancette2025curia,
  title={Curia: A Multi-Modal Foundation Model for Radiology},
  author={Dancette, Corentin and Khlaut, Julien and Saporta, Antoine and Philippe, Helene and Ferreres, Elodie and Callard, Baptiste and Danielou, Th{\'e}o and Alberge, L{\'e}o and Machado, L{\'e}o and Tordjman, Daniel and others},
  journal={arXiv preprint arXiv:2509.06830},
  year={2025}
}