# Curia: A Multi-Modal Foundation Model for Radiology

arXiv: https://arxiv.org/abs/2509.06830 · HTML: https://mlsanity.com/paper/2509.06830/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 45 references that are in the map.
- First posted on arXiv 8 Sep 2025.

## Builds on (most cited first, 18 of 18)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)
- [Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge](https://mlsanity.com/paper/1612.08012/) (2016)
- [Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning](https://mlsanity.com/paper/2112.04489/) (2021)
- [BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once](https://mlsanity.com/paper/2405.12971/) (2024)
- [The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT](https://mlsanity.com/paper/2307.01984/) (2023)
- [MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging](https://mlsanity.com/paper/2410.06542/) (2024)
- [COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning](https://mlsanity.com/paper/2101.07433/) (2021)
- [Foundation Models in Radiology: What, How, When, Why and Why Not](https://mlsanity.com/paper/2411.18730/) (2024)
- [You Don't Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning](https://mlsanity.com/paper/2406.09294/) (2024)
- [RadSAM: Segmenting 3D radiological images with a 2D promptable model](https://mlsanity.com/paper/2504.20837/) (2025)
- [Generation of annotated multimodal ground truth datasets for abdominal medical image registration](https://mlsanity.com/paper/2012.01582/) (2020)
- [TorchSurv: A Lightweight Package for Deep Survival Analysis](https://mlsanity.com/paper/2404.10761/) (2024)
- [ONCOPILOT: A Promptable CT Foundation Model For Solid Tumor Evaluation](https://mlsanity.com/paper/2410.07908/) (2024)

## Built on by (most cited first, 15 of 15)

- [Curia-2: Scaling Self-Supervised Learning for Radiology Foundation Models](https://mlsanity.com/paper/2604.01987/) (2026)
- [Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining](https://mlsanity.com/paper/2605.21906/) (2026)
- [Jolia: Concept-Level Vision-Language Alignment for 3D CT Contrastive Learning](https://mlsanity.com/paper/2606.24570/) (2026)
- [Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge](https://mlsanity.com/paper/2604.11679/) (2026)
- [Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices](https://mlsanity.com/paper/2607.01001/) (2026)
- [Resolution Meets Reduction: Efficient Visual Context for 3D Radiology Report Generation](https://mlsanity.com/paper/2608.08713/) (2026)
- [EvalBlocks: A Modular Pipeline for Rapidly Evaluating Foundation Models in Medical Imaging](https://mlsanity.com/paper/2601.03811/) (2026)
- [Learning How Much, Not Just What: Cross-Patient Burden Order for CT Vision-Language Pretraining](https://mlsanity.com/paper/2608.00231/) (2026)
- [Semantically Calibrated Evidence Composition for CT Vision-Language Learning](https://mlsanity.com/paper/2608.00239/) (2026)
- [Lesion Detection in CT with Frozen Self-Distilled Features: SALT, a Spatially Adaptive Label-Guided Temperature](https://mlsanity.com/paper/2608.05100/) (2026)
- [Curia-MAE: Multi-Modal Multi-Anatomy MAE Pre-Training for 3D Medical Image Segmentation](https://mlsanity.com/paper/2608.05844/) (2026)
- [nnFoundation: 3D Foundation Models for Radiology](https://mlsanity.com/paper/2609.26924/) (2026)
- [Feature Space Guidance for Breast Cancer Classification in DCE-MRI](https://mlsanity.com/paper/2609.32555/) (2026)
- [Anguinus Sculpturae: Compositional Synthesis of Peak-Enhancement Breast DCE-MRI Scans](https://mlsanity.com/paper/2609.33611/) (2026)
- [Med-RADIO: Reducing All Medical Domains Into One via Multi-Teacher Distillation](https://mlsanity.com/paper/2609.37682/) (2026)

## Abstract (arXiv)

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.