Curia：面向放射学的多模态基础模型

摘要

AI辅助的放射学解读主要依赖于狭窄、单一任务的模型。这种方法难以覆盖广泛的成像模式、疾病及放射学发现。基础模型（FMs）展现出跨模态和在低数据环境下广泛泛化的潜力。然而，这一潜力在放射学领域大多尚未实现。我们推出了Curia，这是一个基于某大型医院多年全部横断面成像输出训练的基础模型，据我们所知，这是迄今为止最大的真实世界数据集，包含15万次检查（130TB）。在一个新构建的包含19项任务的外部验证基准上，Curia能够准确识别器官、检测如脑出血和心肌梗死等病症，并预测肿瘤分期的结果。Curia在性能上达到或超越了放射科医生及近期的基础模型，并在跨模态和低数据场景下展现出具有临床意义的新兴特性。为加速研究进展，我们在https://huggingface.co/raidium/curia发布了基础模型的权重。

English

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.

Curia：面向放射学的多模态基础模型

Curia: A Multi-Modal Foundation Model for Radiology

摘要

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