Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical Image Datasets

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Abstract

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. Firstly, 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. Secondly, 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.
Original languageEnglish
Pages (from-to)988-997
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume42
Issue number4
Early online date9 Jan 2019
DOIs
Publication statusPublished - 1 Apr 2020

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