XGeM:用于多式联络医疗数据生成的多提示基础模型.
Daniele Molino1, Francesco Di Feola2, Eliodoro Faiella3
1Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Roma, Italy.
概括
我们开发了XGeM,这是一款用于合成多种医疗数据的多式生成人工智能模型. 这种先进的AI解决了数据稀缺和隐私问题,使得更好的医学成像研究成为可能.
科学领域:
- 医疗成像中的人工智能
- 为医疗保健提供生成性AI
- 多模式数据合成
背景情况:
- 医学成像中的AI提供了潜力,但面临着诸如数据稀缺,隐私和多式联络整合等障碍.
- 当前的生成模型经常产生单模或单向合成数据,缺乏临床一致性.
- 现有的方法在联合合成多种医疗数据模式方面遇到了困难.
研究的目的:
- 介绍XGeM,一个大型多式联络生成模型,用于灵活的,任何对任何医疗数据合成.
- 克服现有的生成模型在同时处理多种数据类型方面的局限性.
- 为解决医疗数据的关键挑战提供一个基础模型.
主要方法:
- 开发了XGeM,一个677亿参数的多式联运生成模型.
- 使用对比学习构建了一个共享的潜空间.
- 实施了一种新的多提示训练策略,用于对任意输入模式子集进行调节.
主要成果:
- 在MIMIC-CXR数据集上,XGeM与五个竞争对手相比表现优越.
- 专家放射科医生通过视觉图灵测试证实了XGeM生成的数据的临床相关性和现实性.
- XGeM有效地支持医疗数据匿名化,阶级不平衡和数据稀缺的挑战.
结论:
- XGeM为多式联络医疗数据合成提供了强大的解决方案,保持了临床一致性.
- 该模型的灵活性和产生连贯多式联通输出的能力解决了当前人工智能的关键局限性.
- XGeM 作为一个基础模型,具有推动医疗人工智能应用的巨大潜力.
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