首个百亿参数胸部X光生成模型，可高保真合成多样化影像。
Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers
- 用1.3亿参数的Transformer模型从头训练，基于120万张影像数据。
- 生成图像经临床专家测试，与真实影像难以区分。
- 支持多病种、多人群、多拍摄视角的可控生成，适合医学研究与模型评估。
我们提出了首个从零开始训练的百亿参数级胸部X光生成基础模型。现有放射影像AI模型在不同患者群体、医疗机构和采集条件下泛化能力差，限制了临床应用。可控的高保真胸部X光合成是扩充临床数据集、评估诊断模型鲁棒性的有效路径。因此，我们构建了目前规模最大的专用生成模型，参数量超过13亿，基于1.2百万张影像及临床专家标注的元数据，训练了1.6万亿个标记。该模型支持跨多个患者亚群、拍摄视角和十余种病灶的可控生成与编辑。此外，我们在合成质量上显著超越现有水平，生成图像对临床专家而言与真实影像无法区分。
原文摘要 · Abstract (English)
We introduce the first generative foundation model for chest radiograph synthesis trained from scratch at the billion-parameter scale. Existing radiographic AI models often suffer from poor generalisation across patient subpopulations, institutions, and acquisition settings, resulting in limited real-world clinical utility. Controlled, high-fidelity synthesis of chest radiographs is a promising path toward diversifying clinical datasets and evaluating the robustness of diagnostic models. Therefore, we present the largest specialist generative foundation model for chest radiographs to date, with over 1.3B parameters, trained for 1.6T tokens on a curated, heterogeneous dataset comprising 1.2M radiographs and clinical expert-guided metadata. Our model supports controllable radiograph generation and editing across multiple demographic subgroups, acquisition views, and a dozen pathologies. Moreover, we significantly advance the state of the art in radiograph synthesis fidelity, producing images that are indistinguishable from real radiographs to clinical experts.
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