Imperial College London
Portrait Picture

Dr Ben Glocker

Faculty of Engineering, Department of Computing

Professor in Machine Learning for Imaging
 
 
 
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Contact

 

+44 (0)20 7594 8334b.glocker Website CV

 
 
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Location

 

377Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Arslan:2018:10.1007/978-3-030-00689-1_1,
author = {Arslan, S and Ktena, SI and Glocker, B and Rueckert, D},
doi = {10.1007/978-3-030-00689-1_1},
publisher = {Springer Verlag},
title = {Graph saliency maps through spectral convolutional networks: application to sex classification with brain connectivity},
url = {http://dx.doi.org/10.1007/978-3-030-00689-1_1},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Graph convolutional networks (GCNs) allow to apply traditional convolution operations in non-Euclidean domains, where data are commonly modelled as irregular graphs. Medical imaging and, in particular, neuroscience studies often rely on such graph representations, with brain connectivity networks being a characteristic example, while ultimately seeking the locus of phenotypic or disease-related differences in the brain. These regions of interest (ROIs) are, then, considered to be closely associated with function and/or behaviour. Driven by this, we explore GCNs for the task of ROI identification and propose a visual attribution method based on class activation mapping. By undertaking a sex classification task as proof of concept, we show that this method can be used to identify salient nodes (brain regions) without prior node labels. Based on experiments conducted on neuroimaging data of more than 5000 participants from UK Biobank, we demonstrate the robustness of the proposed method in highlighting reproducible regions across individuals. We further evaluate the neurobiological relevance of the identified regions based on evidence from large-scale UK Biobank studies.
AU  - Arslan,S
AU  - Ktena,SI
AU  - Glocker,B
AU  - Rueckert,D
DO  - 10.1007/978-3-030-00689-1_1
PB  - Springer Verlag
PY  - 2018///
SN  - 0302-9743
TI  - Graph saliency maps through spectral convolutional networks: application to sex classification with brain connectivity
UR  - http://dx.doi.org/10.1007/978-3-030-00689-1_1
UR  - http://hdl.handle.net/10044/1/63030
ER  -
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