Manifold alignment (MA) is a technique to map many high-dimensional datasets to one shared low-dimensional space. Here we develop a pipeline for using MA to reconstruct high-resolution medical images. We present two key contributions. First, we develop a novel MA scheme in which each high-dimensional dataset can be differently weighted preventing noisier or less informative data from corrupting the aligned embedding. We find that this generalisation improves performance in our experiments in both supervised and unsupervised MA problems. Second, we use the wave kernel signature as a graph descriptor for the unsupervised MA case finding that it significantly outperforms the current state-of-the-art methods and provides higher quality reconstructed magnetic resonance volumes than existing methods.

中文翻译：

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使用波核签名的加权流形对齐来对齐医学图像数据集


流形对齐（MA）是一种将许多高维数据集映射到一个共享的低维空间的技术。在这里，我们开发了一个使用 MA 重建高分辨率医学图像的管道。我们提出了两项​​关键贡献。首先，我们开发了一种新颖的 MA 方案，其中每个高维数据集可以进行不同的加权，以防止噪声较大或信息较少的数据破坏对齐的嵌入。我们发现这种泛化提高了我们在监督和无监督 MA 问题的实验中的性能。其次，我们使用波核签名作为无监督 MA 案例的图形描述符，发现它显着优于当前最先进的方法，并提供比现有方法更高质量的重建磁共振体积。