合成乳房超声图像:一项研究克服医疗数据共享障碍
JiaLe Xu1,2, Qing Hua1,2, XiaoHong Jia1,2
1Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025 Shanghai, China.
Research (Washington, D.C.)
|December 4, 2024
概括
使用深度生成模型CoLDiT合成医疗图像,克服了大数据共享的隐私障碍. 这种方法增强了乳房超声波 (美国) 图像数据集,以改善医疗保健结果.
科学领域:
- 医学成像医学成像
- 医疗保健中的人工智能
- 数据隐私 数据隐私
背景情况:
- 医疗大数据具有改善医疗保健的巨大潜力,但由于隐私问题而未得到充分利用.
- 这些问题限制了跨中心的数据共享和创建大型,多样化的数据集.
- 这阻碍了用于医学诊断的先进AI模型的开发.
研究的目的:
- 开发一种保护隐私的方法来合成医疗数据,特别是乳房超声波 (美国) 图像.
- 克服数据共享障碍,并使更大,更多样化的医疗数据集能够构建.
- 评估合成美国图像在乳腺损伤分类中增强训练数据的有用性.
主要方法:
- 开发了CoLDiT,一种带有变压器骨干的有条件隐性扩散模型,用于生成合成乳房美国图像.
- 在202家医院的5243名患者的9705张真实美国图像的大数据集上训练了COLDiT.
- 通过近邻分析验证了生成图像的隐私,并通过盲目读者研究评估了现实性.
主要成果:
- 在不重复私人信息的情况下,CoLDiT成功地在各种BI-RADS类别中生成了真实的合成乳房美国图像.
- 盲人读者研究显示,在区分真实和合成图像方面表现良好 (AUC为0.53-0.77).
- 用合成图像增强BI-RADS分类训练集的性能与仅使用真实图像 (AUC的P=0.81) 相似.
结论:
- 像CoLDiT这样的模型生成的合成医疗数据提供了一个可行的,保护隐私的解决方案.
- 这项技术可以促进安全的医疗数据共享,克服目前的局限性.
- 它可以更好地利用医疗大数据来推进医疗保健成果和人工智能开发.
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