Metric learning with spectral graph convolutions on brain connectivity networks

By Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante, Martin Rajchl, M. J. Lee, Ben Glocker, Daniel Rueckert. NeuroImage, 2018

375 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7e2297791408263a006febcf195ed05e68ff493b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Daniel Rueckert
2. Ben Glocker
3. Enzo Ferrante
4. Sarah Parisot
5. Dinggang Shen
6. Nathanael Perraudin
7. Daoqiang Zhang
8. Michaël Defferrard

In Cognitive Neuroscience

1. Dinggang Shen
2. Ben Glocker
3. Daniel Rueckert
4. Daoqiang Zhang
5. Sarah Parisot
6. Enzo Ferrante
7. M. J. Lee
8. Mingxia Liu

In Radiology, Nuclear Medicine and Imaging

1. Ben Glocker
2. Daniel Rueckert
3. Dinggang Shen
4. Daoqiang Zhang
5. Sarah Parisot
6. Enzo Ferrante
7. Mingxia Liu
8. Sofia Ira Ktena

Citations in the corpus

• Graph Neural Networks and Their Current Applications in Bioinformatics (2021, positive): “The pioneering work of depth graph networks in brain connection research was done by Ktena et al. (2018), and then this work was further extended about the way of cross-validation (Mirakhorli and Mirakhorli, 2019).”
• DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications (2018, methodology): “…to their versatility, graph neural networks have been used in a variety of tasks, such as identifying diseases from brain connectivity networks [61] or population graphs [62], designing drugs using molecular graphs [63], segmenting 3D point clouds [64], optimizing shapes to be aerodynamic…”
• Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease (2018, methodology): “As an alternative, spectral methods have been used to learn a similarity metric between functional connectivity networks (Ktena et al., 2017), which was applied for Autism Spectrum Disorder (ASD) and sex classification as well as manifold learning (Ktena et al., 2018).”
• Low-Rank Representation for Multi-center Autism Spectrum Disorder Identification (2018, positive): “Besides, we compare MCLRR with 3 state-of-the-art methods for ASD diagnosis, including a graph-based convolutional network [3] with hinge loss (denoted as sGCN-1) and global loss (denoted as sGCN-2), functional connectivity association analysis with leaveone-out classifier (FCA) [4], and a denoising”