一种利用解剖多视图数据对非影像表型进行可解释预测的深度网络
A Deep Network for Explainable Prediction of Non-Imaging Phenotypes using Anatomical Multi-View Data
AI总结：
本文提出可解释多视图网络EMV-Net，通过视图特定特征提取、可学习融合和小波变换跨视图互补信息校准，提升年龄、力量和认知等非影像表型预测性能，并输出注意力校准分数解释解剖结构重要性。
摘要：
大型数据集通常包含多个不同的特征集（即视图），它们提供互补信息，可被多视图学习方法利用以改善结果。我们研究了解剖多视图数据，其中每个大脑解剖结构由多个特征集描述。具体而言，我们关注来自扩散MRI的白质微结构和连接特征集，以及来自结构MRI的灰质面积和厚度特征集。我们研究了应用多视图方法改进非影像表型预测的机器学习方法，这些表型包括人口统计学（年龄）、运动（力量）和认知（图片词汇）。我们提出了一种可解释的多视图网络（EMV-Net），它能够利用不同的解剖视图来提高预测性能。在该网络中，每个单独的解剖视图由一个视图特定的特征提取器处理，从每个视图提取的信息使用可学习权重进行融合。随后是一个基于小波变换的模块，用于获取跨视图的互补信息，然后将其用于校准视图特定信息。此外，校准器产生一个基于注意力的校准分数，以指示解剖结构对解释的重要性。
英文摘要：
Large datasets often contain multiple distinct feature sets, or views, that offer complementary information that can be exploited by multi-view learning methods to improve results. We investigate anatomical multi-view data, where each brain anatomical structure is described with multiple feature sets. In particular, we focus on sets of white matter microstructure and connectivity features from diffusion MRI, as well as sets of gray matter area and thickness features from structural MRI. We investigate machine learning methodology that applies multi-view approaches to improve the prediction of non-imaging phenotypes, including demographics (age), motor (strength), and cognition (picture vocabulary). We present an explainable multi-view network (EMV-Net) that can use different anatomical views to improve prediction performance. In this network, each individual anatomical view is processed by a view-specific feature extractor and the extracted information from each view is fused using a learnable weight. This is followed by a wavelet transform-based module to obtain complementary information across views which is then applied to calibrate the view-specific information. Additionally, the calibrator produces an attention-based calibration score to indicate anatomical structures' importance for interpretation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
