通过挖掘潜在的共享模式信息来进行阿尔茨海默病的跨模式合成和诊断的联合学习框架
Chenhui Wang1, Sirong Piao2, Zhizhong Huang3
1Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, China.
Medical image analysis
|November 23, 2023
概括
这项研究引入了一个新的AI框架,通过合成现实的PET图像来改进使用MRI扫描的早期阿尔茨海默病 (AD) 诊断. 该方法提高了诊断的准确性,并在不同的数据集中很好地概括.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 阿尔茨海默病 (AD) 诊断需要早期检测才能进行有效的干预.
- 功能性正子发射断层扫描 (PET) 的灵敏度高于磁共振成像 (MRI),但成本昂贵,难以获得.
- 利用未标记的PET数据来增强基于MRI的AD诊断至关重要.
研究的目的:
- 开发一种新的联合学习框架,用于无监督的交叉模式合成和AD诊断.
- 通过合成歧视性PET图像来改进MRI的AD诊断.
- 挖掘共享的模式信息,以提高诊断性能.
主要方法:
- 提出一个联合学习框架,整合无监督交叉模式合成和AD诊断.
- 开发了一个无监督的跨模态合成网络,用于使用AdaIN进行3D PET-MRI相互转换.
- 引入了一个可解释的2D卷积神经网络用于AD诊断,使用合成PET或原始MRI.
主要成果:
- 该框架合成了现实的PET图像.
- 在ADNI数据集上,与最先进的ADD诊断方法相比,取得了更高的性能.
- 在外部AIBL和NACC数据集上表现出强大的概括能力.
结论:
- 拟议的框架通过交叉模式合成利用未标记的PET数据,有效地提高了MRI的AD诊断.
- 该方法合成了现实的PET图像,提高了诊断准确度.
- 这种方法显示出对现实世界的临床应用有前途的概括性.
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