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Curia: A Multi-Modal Foundation Model for Radiology
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Curia: A Multi-Modal Foundation Model for Radiology
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AI-assisted radiological interpretation is based on predominantly narrow, single-task models. This approach is impractical for covering the vast spectrum of imaging modalities, diseases, and radiological findings. Foundation models (FMs) hold the promise of broad generalization across modalities and in low-data settings. However, this potential has remained largely unrealized in 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. To accelerate progress, we release our base model's weights at https://huggingface.co/raidium/curia.
Forward citations
Cited by 6 Pith papers
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FlexiCT applies agglomerative pretraining across 2D, 3D, and vision-language stages on a large public CT collection to produce representations that match or exceed specialized models on segmentation, classification, r...
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Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Self-supervised pretraining on 60K clinical-style brain MRIs improves out-of-domain generalization on classification, segmentation, and regression tasks, with hybrid objectives and small models showing strong results.
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Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices
Benchmark finds segmentation dominates volume and stage tasks while classifier choice dominates survival, histology, and age prediction, recommending Curia with tumor segmentation and CatBoost as a safe default.
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Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining
FlexiCT provides CT foundation models via agglomerative pretraining on 266227 volumes from 56 datasets that match or exceed task-specific models on five task families while organizing embeddings along tumor-stage gradients.
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Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Self-supervised pretraining on large unlabeled clinical brain MRI data improves generalization to out-of-domain clinical tasks over supervised in-domain training, with task-specific optimal objectives and limited bene...
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