arXiv 2509.039032025-09-04cs.CV

一种用于胸部X光片的生成式基础模型

A Generative Foundation Model for Chest Radiography

Department of Radiation Oncology, Stanford University School of Medicine（斯坦福大学医学院放射肿瘤科）；Department of Computer Science, School of Computing and Data Science, The University of Hong Kong（香港大学计算机科学系）；Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University（康奈尔大学兽医学院人口医学与诊断科学系）；Department of Radiology, The First Affiliated Hospital of Jinan University（暨南大学第一附属医院放射科）；Department of Radiology, Stanford University School of Medicine（斯坦福大学医学院放射科）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程系）；Department of Biomedical Informatics, Harvard Medical School（哈佛医学院生物医学信息学系）

Yuanfeng Ji, Dan Lin, Xiyue Wang, Lu Zhang, Wenhui Zhou, Chongjian Ge, Ruihang Chu, Xiaoli Yang, Junhan Zhao, Junsong Chen, Xiangde Luo, Sen Yang, Jin Fang, Ping Luo, Ruijiang Li

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AI总结：

针对医学标注图像稀缺问题，提出基于潜在扩散Transformer的生成式视觉-语言基础模型ChexGen，支持多引导的胸部X光合成，在小样本医疗任务中提升性能，还可缓解人口统计学偏差以增强模型公平性。

摘要：

标注良好的多样化医学图像稀缺是医疗领域开发可靠AI模型的主要障碍。自然图像生成式基础模型已取得重大技术进展。我们开发了ChexGen——一种生成式视觉-语言基础模型，它引入了统一框架，支持文本、掩码和边界框引导的胸部X光片合成。ChexGen基于潜在扩散Transformer架构构建，在迄今最大的精选胸部X光数据集上预训练，该数据集包含96万张X光片-报告对。经专家评估和量化指标验证，ChexGen可实现精准的X光片合成。我们展示了ChexGen在训练数据增强和有监督预训练方面的效用：仅使用小部分训练数据，就能在疾病分类、检测和分割任务中实现性能提升。此外，我们的模型可构建多样化患者队列，通过检测并缓解人口统计学偏差来增强模型公平性。本研究证实了生成式基础模型在构建更精准、数据高效且公平的医疗AI系统方面的变革性作用。

英文摘要：

The scarcity of well-annotated diverse medical images is a major hurdle for developing reliable AI models in healthcare. Substantial technical advances have been made in generative foundation models for natural images. Here we develop `ChexGen', a generative vision-language foundation model that introduces a unified framework for text-, mask-, and bounding box-guided synthesis of chest radiographs. Built upon the latent diffusion transformer architecture, ChexGen was pretrained on the largest curated chest X-ray dataset to date, consisting of 960,000 radiograph-report pairs. ChexGen achieves accurate synthesis of radiographs through expert evaluations and quantitative metrics. We demonstrate the utility of ChexGen for training data augmentation and supervised pretraining, which led to performance improvements across disease classification, detection, and segmentation tasks using a small fraction of training data. Further, our model enables the creation of diverse patient cohorts that enhance model fairness by detecting and mitigating demographic biases. Our study supports the transformative role of generative foundation models in building more accurate, data-efficient, and equitable medical AI systems.

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