A Heterogeneous Graph Based Framework for Multimodal Neuroimaging Fusion Learning

• URL: http://arxiv.org/abs/2110.08465v1
• Date: Sat, 16 Oct 2021 04:15:33 GMT
• Title: A Heterogeneous Graph Based Framework for Multimodal Neuroimaging Fusion Learning
• Authors: Gen Shi, Yifan Zhu, Wenjin Liu, Xuesong Li
• Abstract summary: We present a Heterogeneous Graph neural network for Multimodal fusion learning (HGM) Traditional GNN-based models usually assume the brain network is a homogeneous graph with single type of nodes and edges. Our results on two datasets show the superiority of proposed model over other multimodal methods for disease prediction task.
• Score: 6.23207187065507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Here, we present a Heterogeneous Graph neural network for Multimodal neuroimaging fusion learning (HGM). Traditional GNN-based models usually assume the brain network is a homogeneous graph with single type of nodes and edges. However, vast literatures have shown the heterogeneity of the human brain especially between the two hemispheres. Homogeneous brain network is insufficient to model the complicated brain state. Therefore, in this work we firstly model the brain network as heterogeneous graph with multi-type nodes (i.e., left and right hemispheric nodes) and multi-type edges (i.e., intra- and inter-hemispheric edges). Besides, we also propose a self-supervised pre-training strategy based on heterogeneou brain network to address the overfitting problem due to the complex model and small sample size. Our results on two datasets show the superiority of proposed model over other multimodal methods for disease prediction task. Besides, ablation experiments show that our model with pre-training strategy can alleviate the problem of limited training sample size.

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)
• HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification [1.7495515703051119]
  We propose a hierarchical dynamic graph representation learning (HDGL) model, which is the first model designed to address all the aforementioned challenges. We evaluate the performance of the proposed model on the ABIDE and ADHD-200 datasets.
  arXiv  Detail & Related papers  (2023-11-06T06:29:23Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• DBGDGM: Dynamic Brain Graph Deep Generative Model [63.23390833353625]
  Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as functional connectivity networks (FCNs), encode temporal relationships which can serve as useful biomarkers for understanding brain function and dysfunction. Previous works, however, ignore the temporal dynamics of the brain and focus on static graphs. We propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings.
  arXiv  Detail & Related papers  (2023-01-26T20:45:30Z)
• Investigating Neuron Disturbing in Fusing Heterogeneous Neural Networks [6.389882065284252]
  In this paper, we reveal the phenomenon of neuron disturbing, where neurons from heterogeneous local models interfere with each other mutually. We propose an experimental method that excludes neuron disturbing and fuses neural networks via adaptively selecting a local model, called AMS, to execute the prediction.
  arXiv  Detail & Related papers  (2022-10-24T06:47:48Z)
• Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model [64.29487107585665]
  Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Here, we propose an interpretable hierarchical signed graph representation learning model to extract graph-level representations from brain functional networks. In order to further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning.
  arXiv  Detail & Related papers  (2022-07-14T20:03:52Z)
• Functional2Structural: Cross-Modality Brain Networks Representation Learning [55.24969686433101]
  Graph mining on brain networks may facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. We propose a novel graph learning framework, known as Deep Signed Brain Networks (DSBN), with a signed graph encoder. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets.
  arXiv  Detail & Related papers  (2022-05-06T03:45:36Z)
• Multi-modal learning for predicting the genotype of glioma [14.93152817415408]
  The isocitrate dehydrogenase (IDH) gene mutation is an essential biomarker for the diagnosis and prognosis of glioma. It is promising to better predict glioma genotype by integrating focal tumor image and geometric features with brain network features derived from MRI. We propose a multi-modal learning framework using three separate encoders to extract features of focal tumor image, tumor geometrics and global brain networks.
  arXiv  Detail & Related papers  (2022-03-21T10:20:04Z)
• Self-Supervised Graph Representation Learning for Neuronal Morphologies [75.38832711445421]
  We present GraphDINO, a data-driven approach to learn low-dimensional representations of 3D neuronal morphologies from unlabeled datasets. We show, in two different species and across multiple brain areas, that this method yields morphological cell type clusterings on par with manual feature-based classification by experts. Our method could potentially enable data-driven discovery of novel morphological features and cell types in large-scale datasets.
  arXiv  Detail & Related papers  (2021-12-23T12:17:47Z)
• Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis [17.48272758284748]
  Graph Neural Networks (GNNs) have become a de facto model for analyzing graph-structured data. We develop a novel multiview GNN for multimodal brain networks. In particular, we regard each modality as a view for brain networks and employ contrastive learning for multimodal fusion.
  arXiv  Detail & Related papers  (2021-07-07T13:49:57Z)
• Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates [0.0]
  A connectional brain template (CBT) is a normalized graph-based representation of a population of brain networks. Deep Graph Normalizer (DGN) is the first geometric deep learning architecture for normalizing a population of MVBNs. DGN learns how to fuse multi-view brain networks while capturing non-linear patterns across subjects.
  arXiv  Detail & Related papers  (2020-12-28T08:01:49Z)

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.