Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain Graph

• URL: http://arxiv.org/abs/2009.11058v1
• Date: Wed, 23 Sep 2020 11:23:08 GMT
• Title: Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain Graph
• Authors: Alaa Bessadok, Mohamed Ali Mahjoub and Islem Rekik
• Abstract summary: 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.
• Score: 1.2891210250935146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Several works based on Generative Adversarial Networks (GAN) have been recently proposed to predict a set of medical images from a single modality (e.g, FLAIR MRI from T1 MRI). However, such frameworks are primarily designed to operate on images, limiting their generalizability to non-Euclidean geometric data such as brain graphs. While a growing number of connectomic studies has demonstrated the promise of including brain graphs for diagnosing neurological disorders, no geometric deep learning work was designed for multiple target brain graphs prediction from a source brain graph. Despite the momentum the field of graph generation has gained in the last two years, existing works have two critical drawbacks. First, the bulk of such works aims to learn one model for each target domain to generate from a source domain. Thus, they have a limited scalability in jointly predicting multiple target domains. Second, they merely consider the global topological scale of a graph (i.e., graph connectivity structure) and overlook the local topology at the node scale of a graph (e.g., how central a node is in the graph). To meet these challenges, we introduce MultiGraphGAN architecture, which not only predicts multiple brain graphs from a single brain graph but also preserves the topological structure of each target graph to predict. 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, (iii) introducing a topological loss to force the reconstruction of topologically sound target brain graphs. Our MultiGraphGAN significantly outperformed its variants thereby showing its great potential in multi-view brain graph generation from a single graph.

Related papers

• Graph Neural Networks for Brain Graph Learning: A Survey [53.74244221027981]
  Graph neural networks (GNNs) have demonstrated a significant advantage in mining graph-structured data. GNNs to learn brain graph representations for brain disorder analysis has recently gained increasing attention. In this paper, we aim to bridge this gap by reviewing brain graph learning works that utilize GNNs.
  arXiv  Detail & Related papers  (2024-06-01T02:47:39Z)
• A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression [48.97251676778599]
  In medical imaging, population graphs have demonstrated promising results, mostly for classification tasks. extracting population graphs is a non-trivial task and can significantly impact the performance of Graph Neural Networks (GNNs) In this work, we highlight the importance of a meaningful graph construction and experiment with different population-graph construction methods.
  arXiv  Detail & Related papers  (2023-09-26T10:30:45Z)
• Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming [48.99614465020678]
  We introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming. This mechanism enables G-Zoom to explore and extract self-supervision signals from a graph from multiple scales. We have conducted extensive experiments on real-world datasets, and the results demonstrate that our proposed model outperforms state-of-the-art methods consistently.
  arXiv  Detail & Related papers  (2021-11-20T22:45:53Z)
• Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning [0.0]
  We propose Learn to SuperResolve Brain Graphs with Knowledge Distillation Network (L2S-KDnet) to superresolve brain graphs. Our teacher network is a graph encoder-decoder that firstly learns the LR brain graph embeddings, and secondly learns how to align the resulting latent representations to the HR ground truth data distribution. Next, our student network learns the knowledge of the aligned brain graphs as well as the topological structure of the predicted HR graphs transferred from the teacher.
  arXiv  Detail & Related papers  (2021-10-06T09:31:44Z)
• Non-isomorphic Inter-modality Graph Alignment and Synthesis for Holistic Brain Mapping [1.433758865948252]
  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.
  arXiv  Detail & Related papers  (2021-06-30T08:59:55Z)
• 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)
• Heterogeneous Similarity Graph Neural Network on Electronic Health Records [74.66674469510251]
  We propose Heterogeneous Similarity Graph Neural Network (HSGNN) to analyze EHRs with a novel heterogeneous GNN. Our framework consists of two parts: one is a preprocessing method and the other is an end-to-end GNN. The GNN takes all homogeneous graphs as input and fuses all of them into one graph to make a prediction.
  arXiv  Detail & Related papers  (2021-01-17T23:14:29Z)
• 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)
• Geom-GCN: Geometric Graph Convolutional Networks [15.783571061254847]
  We propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses. The proposed aggregation scheme is permutation-invariant and consists of three modules, node embedding, structural neighborhood, and bi-level aggregation. We also present an implementation of the scheme in graph convolutional networks, termed Geom-GCN, to perform transductive learning on graphs.
  arXiv  Detail & Related papers  (2020-02-13T00:03:09Z)
• 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.