Curia：一個多模態的放射學基礎模型

摘要

AI輔助的放射學解讀主要基於狹窄的單任務模型。這種方法在涵蓋廣泛的影像模式、疾病和放射學發現方面顯得不太實際。基礎模型（FMs）展現了跨模式和低數據環境下的廣泛泛化潛力。然而，這一潛力在放射學領域尚未得到充分實現。我們介紹了Curia，這是一個基於一家大型醫院多年來所有斷層影像輸出訓練的基礎模型，據我們所知，這是迄今為止最大的真實世界數據集，涵蓋了150,000次檢查（130 TB）。在一個新策劃的19項外部驗證基準測試中，Curia準確識別器官，檢測如腦出血和心肌梗塞等病症，並預測腫瘤分期的結果。Curia達到或超越了放射科醫生和近期基礎模型的表現，並在跨模式和低數據環境中展現出具有臨床意義的湧現特性。為加速進展，我們在https://huggingface.co/raidium/curia上發布了基礎模型的權重。

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：一個多模態的放射學基礎模型

Curia: A Multi-Modal Foundation Model for Radiology

摘要

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