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

@article{Parisot:2018:10.1016/j.media.2018.06.001,
author = {Parisot, S and Ktena, SI and Ferrante, E and Lee, M and Guerrero, R and Glocker, B and Rueckert, D},
doi = {10.1016/j.media.2018.06.001},
journal = {Medical Image Analysis},
pages = {117--130},
title = {Disease prediction using graph convolutional networks: application to Autism Spectrum Disorder and Alzheimer's disease},
url = {http://dx.doi.org/10.1016/j.media.2018.06.001},
volume = {48},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Graphs are widely used as a natural framework that captures interactionsbetween individual elements represented as nodes in a graph. In medicalapplications, specifically, nodes can represent individuals within apotentially large population (patients or healthy controls) accompanied by aset of features, while the graph edges incorporate associations betweensubjects in an intuitive manner. This representation allows to incorporate thewealth of imaging and non-imaging information as well as individual subjectfeatures simultaneously in disease classification tasks. Previous graph-basedapproaches for supervised or unsupervised learning in the context of diseaseprediction solely focus on pairwise similarities between subjects, disregardingindividual characteristics and features, or rather rely on subject-specificimaging feature vectors and fail to model interactions between them. In thispaper, we present a thorough evaluation of a generic framework that leveragesboth imaging and non-imaging information and can be used for brain analysis inlarge populations. This framework exploits Graph Convolutional Networks (GCNs)and involves representing populations as a sparse graph, where its nodes areassociated with imaging-based feature vectors, while phenotypic information isintegrated as edge weights. The extensive evaluation explores the effect ofeach individual component of this framework on disease prediction performanceand further compares it to different baselines. The framework performance istested on two large datasets with diverse underlying data, ABIDE and ADNI, forthe prediction of Autism Spectrum Disorder and conversion to Alzheimer'sdisease, respectively. Our analysis shows that our novel framework can improveover state-of-the-art results on both databases, with 70.4% classificationaccuracy for ABIDE and 80.0% for ADNI.
AU  - Parisot,S
AU  - Ktena,SI
AU  - Ferrante,E
AU  - Lee,M
AU  - Guerrero,R
AU  - Glocker,B
AU  - Rueckert,D
DO  - 10.1016/j.media.2018.06.001
EP  - 130
PY  - 2018///
SN  - 1361-8415
SP  - 117
TI  - Disease prediction using graph convolutional networks: application to Autism Spectrum Disorder and Alzheimer's disease
T2  - Medical Image Analysis
UR  - http://dx.doi.org/10.1016/j.media.2018.06.001
UR  - http://arxiv.org/abs/1806.01738v1
UR  - http://hdl.handle.net/10044/1/60545
VL  - 48
ER  -
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