用深度学习挖掘多模态数据中的共现与独有变化模式
DeepJIVE: Learning Joint and Individual Variation Explained from Multimodal Data Using Deep Learning
- 基于深度网络构建联合与个体变异模型，突破传统方法局限
- 在真实阿尔茨海默病数据中发现淀粉样蛋白与核磁共振图像的生物相关关联
- 适合需要解析多模态数据深层结构的研究者使用
传统多模态数据整合方法虽能全面评估各数据类型间的共享或独特结构，但存在难以处理高维数据及识别非线性结构等缺陷。本文提出DeepJIVE，一种基于深度学习的联合与个体变异解释（Joint and Individual Variance Explained, JIVE）方法。通过数学推导和合成及真实世界1D、2D、3D数据集的实验验证，探索了实现身份约束与正交性约束的不同策略，最终得到三种可行的损失函数。结果表明，DeepJIVE可有效揭示多模态数据的联合与个体变异。将其应用于阿尔茨海默病神经影像计划（ADNI）数据，成功识别出淀粉样蛋白正电子发射断层扫描（PET）与磁共振（MR）图像间的生物学合理协变模式。结论：DeepJIVE可作为多模态数据分析的有效工具。
原文摘要 · Abstract (English)
Conventional multimodal data integration methods provide a comprehensive assessment of the shared or unique structure within each individual data type but suffer from several limitations such as the inability to handle high-dimensional data and identify nonlinear structures. In this paper, we introduce DeepJIVE, a deep-learning approach to performing Joint and Individual Variance Explained (JIVE). We perform mathematical derivation and experimental validations using both synthetic and real-world 1D, 2D, and 3D datasets. Different strategies of achieving the identity and orthogonality constraints for DeepJIVE were explored, resulting in three viable loss functions. We found that DeepJIVE can successfully uncover joint and individual variations of multimodal datasets. Our application of DeepJIVE to the Alzheimer's Disease Neuroimaging Initiative (ADNI) also identified biologically plausible covariation patterns between the amyloid positron emission tomography (PET) and magnetic resonance (MR) images. In conclusion, the proposed DeepJIVE can be a useful tool for multimodal data analysis.
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