A Comparative Study of Graph Neural Networks for Shape Classification in Neuroimaging

• URL: http://arxiv.org/abs/2210.16670v1
• Date: Sat, 29 Oct 2022 19:03:01 GMT
• Title: A Comparative Study of Graph Neural Networks for Shape Classification in Neuroimaging
• Authors: Nairouz Shehata, Wulfie Bain, Ben Glocker
• Abstract summary: We present an overview of the current state-of-the-art in geometric deep learning for shape classification in neuroimaging. We find that using FPFH as node features substantially improves GNN performance and generalisation to out-of-distribution data. We then confirm these results hold for a clinically relevant task, using the classification of Alzheimer's disease.
• Score: 17.775145204666874
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph neural networks have emerged as a promising approach for the analysis of non-Euclidean data such as meshes. In medical imaging, mesh-like data plays an important role for modelling anatomical structures, and shape classification can be used in computer aided diagnosis and disease detection. However, with a plethora of options, the best architectural choices for medical shape analysis using GNNs remain unclear. We conduct a comparative analysis to provide practitioners with an overview of the current state-of-the-art in geometric deep learning for shape classification in neuroimaging. Using biological sex classification as a proof-of-concept task, we find that using FPFH as node features substantially improves GNN performance and generalisation to out-of-distribution data; we compare the performance of three alternative convolutional layers; and we reinforce the importance of data augmentation for graph based learning. We then confirm these results hold for a clinically relevant task, using the classification of Alzheimer's disease.

Related papers

• Classification of developmental and brain disorders via graph convolutional aggregation [6.6356049194991815]
  We introduce an aggregator normalization graph convolutional network by leveraging aggregation in graph sampling. The proposed model learns discriminative graph node representations by incorporating both imaging and non-imaging features into the graph nodes and edges. We benchmark our model against several recent baseline methods on two large datasets, Autism Brain Imaging Data Exchange (ABIDE) and Alzheimer's Disease Neuroimaging Initiative (ADNI)
  arXiv  Detail & Related papers  (2023-11-13T14:36:29Z)
• Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification [0.0]
  We introduce a novel model that combines GNNs and edge convolution, leveraging the interconnectedness of RGB channel feature values to strongly represent connections between crucial graph nodes. Our proposed model performs on par with state-of-the-art Deep Neural Networks (DNNs) but does so with 1000 times fewer parameters, resulting in reduced training time and data requirements.
  arXiv  Detail & Related papers  (2023-07-24T13:39:21Z)
• Transferability of coVariance Neural Networks and Application to Interpretable Brain Age Prediction using Anatomical Features [119.45320143101381]
  Graph convolutional networks (GCN) leverage topology-driven graph convolutional operations to combine information across the graph for inference tasks. We have studied GCNs with covariance matrices as graphs in the form of coVariance neural networks (VNNs) VNNs inherit the scale-free data processing architecture from GCNs and here, we show that VNNs exhibit transferability of performance over datasets whose covariance matrices converge to a limit object.
  arXiv  Detail & Related papers  (2023-05-02T22:15:54Z)
• Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer's Disease using EEG Data [9.601125513491835]
  We propose a novel adaptive gated graph convolutional network (AGGCN) that can provide explainable predictions. AGGCN adaptively learns graph structures by combining convolution-based node feature enhancement with a correlation-based measure of power spectral density similarity.
  arXiv  Detail & Related papers  (2023-04-12T14:13:09Z)
• Graph Neural Operators for Classification of Spatial Transcriptomics Data [1.408706290287121]
  We propose a study incorporating various graph neural network approaches to validate the efficacy of applying neural operators towards prediction of brain regions in mouse brain tissue samples. We were able to achieve an F1 score of nearly 72% for the graph neural operator approach which outperformed all baseline and other graph network approaches.
  arXiv  Detail & Related papers  (2023-02-01T18:32:06Z)
• Benchmarking Graph Neural Networks for FMRI analysis [0.0]
  Graph Neural Networks (GNNs) have emerged as a powerful tool to learn from graph-structured data. We study and evaluate the performance of five popular GNN architectures in diagnosing major depression disorder and autism spectrum disorder. We highlight that creating optimal graph structures for functional brain data is a major bottleneck hindering the performance of GNNs.
  arXiv  Detail & Related papers  (2022-11-16T14:16:54Z)
• DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning [58.94034282469377]
  We propose DynDepNet, a novel method for learning the optimal time-varying dependency structure of fMRI data induced by downstream prediction tasks. Experiments on real-world fMRI datasets, for the task of sex classification, demonstrate that DynDepNet achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2022-09-27T16:32:11Z)
• 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)
• 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)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis [49.05541693243502]
  We develop a framework for analyzing fMRI data using the Graph Isomorphism Network (GIN) One of the important contributions of this paper is the observation that the GIN is a dual representation of convolutional neural network (CNN) in the graph space. We exploit CNN-based saliency map techniques for the GNN, which we tailor to the proposed GIN with one-hot encoding.
  arXiv  Detail & Related papers  (2020-01-10T23:40:09Z)

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.