Addressing imaging accessibility by cross-modality transfer learning
Addressing imaging accessibility by cross-modality transfer learning
复制标题
通过跨模态迁移学习解决成像可访问性问题
DOI：
10.1117/12.2611791
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Li, Jing
中科院分区：
文献类型：
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作者：
Zheng, Zhiyang;Su, Yi;Chen, Kewei;Weidman, David A.;Wu, Teresa;Lo, Shihchung;Lure, Fleming;Li, Jing
多模式图像通常用于疾病的诊断/预后，例如阿尔茨海默病(AD)，但具有不同程度的可及性和准确性。在护理标准中使用了磁共振成像，因此患者具有很高的可及性。另一方面，AD的病理标志如淀粉样蛋白-PET和tau-PET的成像由于成本和其他实际限制而可及性较低，尽管它们有望提供比标准临床MRI更高的诊断/预后准确性。提出了一种基于高可及性标准成像模式(Mod_HA)的跨模式迁移学习(CMTL)方法，其训练策略不仅利用了标准高可及性成像模式(Mod_HA)的数据，还利用了从基于高级可及性成像模式(Mod_LA)的模型中传来的知识。我们使用阿尔茨海默病神经成像倡议(ADNI)数据集应用CMTL预测轻度认知障碍(MCI)患者向AD的转化，通过利用基于tau-PET(Mod_LA)的模型传递的知识，展示了基于MRI(Mod_HA)的模型的性能改进。
Multi-modality images usually exist for diagnosis/prognosis of a disease, such as Alzheimer’s Disease (AD), but with different levels of accessibility and accuracy. MRI is used in the standard of care, thus having high accessibility to patients. On the other hand, imaging of pathologic hallmarks of AD such as amyloid-PET and tau-PET has low accessibility due to cost and other practical constraints, even though they are expected to provide higher diagnostic/prognostic accuracy than standard clinical MRI. We proposed Cross-Modality Transfer Learning (CMTL) for accurate diagnosis/prognosis based on standard imaging modality with high accessibility (mod_HA), with a novel training strategy of using not only data of mod_HA but also knowledge transferred from the model based on advanced imaging modality with low accessibility (mod_LA). We applied CMTL to predict conversion of individuals with Mild Cognitive Impairment (MCI) to AD using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) datasets, demonstrating improved performance of the MRI (mod_HA)-based model by leveraging the knowledge transferred from the model based on tau-PET (mod_LA).