Скачать или смотреть #ReMI

#GraphNeuralNetworks #MultiGraphFusion #RecurrentNeuralNetworks #GNNs #MICCAI2021
This work is presented at the international class A1 MICCAI 2021 conference (https://miccai2021.org/en/).

#arXiv paper link: https://arxiv.org/abs/2110.03453

You can download the GitHub code at https://github.com/basiralab/ReMI-Net.

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Abstract
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Learninghowtoestimateaconnectionalbraintemplate(CBT) from a population of brain multigraphs, where each graph (e.g., functional) quantifies a particular relationship between pairs of brain regions of interest (ROIs), allows to pin down the unique connectivity patterns shared across individuals. Specifically, a CBT is viewed as an integral representation of a set of highly heterogeneous graphs and ideally meeting the centeredness (i.e., minimum distance to all graphs in the population) and discriminativeness (i.e., distinguishes the healthy from the disordered population) criteria. So far, existing works have been limited to only integrating and fusing a population of brain multigraphs acquired at a single time- point. In this paper, we unprecedentedly tackle the question: “Given a baseline multigraph population, can we learn how to integrate and forecast its CBT rep- resentations at follow-up timepoints?” Addressing such question is of paramount in predicting common alternations across healthy and disordered populations. To fill this gap, we propose Recurrent Multigraph Integrator Network (ReMI-Net), the first graph recurrent neural network which infers the baseline CBT of an input population t1 and predicts its longitudinal evolution over time. Our ReMI-Net is composed of recurrent neural blocks with graph convolutional layers using a cross-node message passing to first learn hidden-states embeddings of each CBT node (i.e., brain region of interest) and then predict its evolution at the consecutive timepoint. Moreover, we design a novel time-dependent loss to regu- larize the CBT evolution trajectory over time and further introduce a cyclic recur- sion and learnable normalization layer to generate well-centered CBTs from time- dependent hidden-state embeddings. Finally, we derive the CBT adjacency matrix from the learned hidden state graph representation. ReMI-Net significantly outperformed benchmark methods in both centeredness and discriminative connectional biomarker discovery criteria in demented patients.