tags:
- medical
license: other
license_name: research-only-rail-m
model-index:
- name: Curia
results:
- task:
type: classification
dataset:
type: CuriaBench
name: CuriaBench Anatomy Recognition
metrics:
- name: Accuracy
type: accuracy
value: 98.1
datasets:
- raidium/CuriaBench
🌟 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}
}