Non-isomorphic Inter-modality Graph Alignment and Synthesis for Holistic Brain Mapping

• URL: http://arxiv.org/abs/2107.06281v1
• Date: Wed, 30 Jun 2021 08:59:55 GMT
• Title: Non-isomorphic Inter-modality Graph Alignment and Synthesis for Holistic Brain Mapping
• Authors: Islem Mhiri and Ahmed Nebli and Mohamed Ali Mahjoub and Islem Rekik
• Abstract summary: We propose an inter-modality aligner of non-isomorphic graphs (IMANGraphNet) framework to infer a target graph modality based on a given modality. Our three core contributions lie in (i) predicting a target graph (e.g., functional) from a source graph (e.g., morphological) based on a novel graph generative adversarial network (gGAN) Our comprehensive experiments on predicting functional from morphological graphs demonstrate the outperformance of IMANGraphNet in comparison with its variants.
• Score: 1.433758865948252
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Brain graph synthesis marked a new era for predicting a target brain graph from a source one without incurring the high acquisition cost and processing time of neuroimaging data. However, existing multi-modal graph synthesis frameworks have several limitations. First, they mainly focus on generating graphs from the same domain (intra-modality), overlooking the rich multimodal representations of brain connectivity (inter-modality). Second, they can only handle isomorphic graph generation tasks, limiting their generalizability to synthesizing target graphs with a different node size and topological structure from those of the source one. More importantly, both target and source domains might have different distributions, which causes a domain fracture between them (i.e., distribution misalignment). To address such challenges, we propose an inter-modality aligner of non-isomorphic graphs (IMANGraphNet) framework to infer a target graph modality based on a given modality. Our three core contributions lie in (i) predicting a target graph (e.g., functional) from a source graph (e.g., morphological) based on a novel graph generative adversarial network (gGAN); (ii) using non-isomorphic graphs for both source and target domains with a different number of nodes, edges and structure; and (iii) enforcing the predicted target distribution to match that of the ground truth graphs using a graph autoencoder to relax the designed loss oprimization. To handle the unstable behavior of gGAN, we design a new Ground Truth-Preserving (GT-P) loss function to guide the generator in learning the topological structure of ground truth brain graphs. Our comprehensive experiments on predicting functional from morphological graphs demonstrate the outperformance of IMANGraphNet in comparison with its variants. This can be further leveraged for integrative and holistic brain mapping in health and disease.

Related papers

• InstructG2I: Synthesizing Images from Multimodal Attributed Graphs [50.852150521561676]
  We propose a graph context-conditioned diffusion model called InstructG2I. InstructG2I first exploits the graph structure and multimodal information to conduct informative neighbor sampling. A Graph-QFormer encoder adaptively encodes the graph nodes into an auxiliary set of graph prompts to guide the denoising process.
  arXiv  Detail & Related papers  (2024-10-09T17:56:15Z)
• Graph Condensation via Receptive Field Distribution Matching [61.71711656856704]
  This paper focuses on creating a small graph to represent the original graph, so that GNNs trained on the size-reduced graph can make accurate predictions. We view the original graph as a distribution of receptive fields and aim to synthesize a small graph whose receptive fields share a similar distribution.
  arXiv  Detail & Related papers  (2022-06-28T02:10:05Z)
• Learning Graph Structure from Convolutional Mixtures [119.45320143101381]
  We propose a graph convolutional relationship between the observed and latent graphs, and formulate the graph learning task as a network inverse (deconvolution) problem. In lieu of eigendecomposition-based spectral methods, we unroll and truncate proximal gradient iterations to arrive at a parameterized neural network architecture that we call a Graph Deconvolution Network (GDN) GDNs can learn a distribution of graphs in a supervised fashion, perform link prediction or edge-weight regression tasks by adapting the loss function, and they are inherently inductive.
  arXiv  Detail & Related papers  (2022-05-19T14:08:15Z)
• StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and Synthesis [1.6114012813668934]
  We propose a multi-resolution StairwayGraphNet (SG-Net) framework to infer a target graph modality based on a given modality and super-resolve brain graphs in both inter and intra domains. Our SG-Net is grounded in three main contributions: (i) predicting a target graph from a source one based on a novel graph generative adversarial network in both inter and intra domains, (ii) generating high-resolution brain graphs without resorting to the time consuming and expensive MRI processing steps, and (iii) enforcing the source distribution to match that of the ground truth graphs.
  arXiv  Detail & Related papers  (2021-10-06T09:49:38Z)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)
• Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural Network [1.6114012813668934]
  We introduce topoGAN architecture, which jointly predicts multiple brain graphs from a single brain graph. Our three key innovations are: (i) designing a novel graph adversarial auto-encoder for predicting multiple brain graphs from a single one, (ii) clustering the encoded source graphs in order to handle the mode collapse issue of GAN and (iii) introducing a topological loss to force the prediction of topologically sound target brain graphs.
  arXiv  Detail & Related papers  (2021-05-06T10:20:45Z)
• Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain Graph [1.2891210250935146]
  We introduce MultiGraphGAN architecture, which predicts multiple brain graphs from a single brain graph. Its three core contributions lie in: (i) designing a graph adversarial auto-encoder for jointly predicting brain graphs from a single one, (ii) handling the mode collapse problem of GAN by clustering the encoded source graphs and proposing a cluster-specific decoder.
  arXiv  Detail & Related papers  (2020-09-23T11:23:08Z)
• MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning [31.42901131602713]
  We propose a framework for graph neural networks with multiresolution Haar-like wavelets, or MathNet, with interrelated convolution and pooling strategies. The proposed MathNet outperforms various existing GNN models, especially on big data sets.
  arXiv  Detail & Related papers  (2020-07-22T05:00:59Z)
• Multilevel Graph Matching Networks for Deep Graph Similarity Learning [79.3213351477689]
  We propose a multi-level graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects. To compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks. Comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baseline models on both the graph-graph classification and graph-graph regression tasks.
  arXiv  Detail & Related papers  (2020-07-08T19:48:19Z)
• Graph Pooling with Node Proximity for Hierarchical Representation Learning [80.62181998314547]
  We propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology. Results show that the proposed graph pooling strategy is able to achieve state-of-the-art performance on a collection of public graph classification benchmark datasets.
  arXiv  Detail & Related papers  (2020-06-19T13:09:44Z)
• Differentiable Graph Module (DGM) for Graph Convolutional Networks [44.26665239213658]
  Differentiable Graph Module (DGM) is a learnable function that predicts edge probabilities in the graph which are optimal for the downstream task. We provide an extensive evaluation of applications from the domains of healthcare (disease prediction), brain imaging (age prediction), computer graphics (3D point cloud segmentation), and computer vision (zero-shot learning)
  arXiv  Detail & Related papers  (2020-02-11T12:59:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.