TY - GEN
T1 - Graph saliency maps through spectral convolutional networks
T2 - 2nd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018 and 1st International Workshop on Integrating Medical Imaging and Non-Imaging Modalities, Beyond MIC 2018 Held in Conjunction with 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018
AU - Arslan, Salim
AU - Ktena, Sofia Ira
AU - Glocker, Ben
AU - Rueckert, Daniel
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - 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.
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.
UR - https://www.scopus.com/pages/publications/85054378614
U2 - 10.1007/978-3-030-00689-1_1
DO - 10.1007/978-3-030-00689-1_1
M3 - Conference contribution
AN - SCOPUS:85054378614
SN - 9783030006884
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 13
BT - Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities - 2nd International Workshop, GRAIL 2018 and 1st International Workshop, Beyond MIC 2018 Held in Conjunction with MICCAI 2018, Proceedings
A2 - Stoyanov, Danail
A2 - Sotiras, Aristeidis
A2 - Papiez, Bartlomiej
A2 - Dalca, Adrian V.
A2 - Martel, Anne
A2 - Parisot, Sarah
A2 - Ferrante, Enzo
A2 - Maier-Hein, Lena
A2 - Sabuncu, Mert R.
A2 - Shen, Li
A2 - Taylor, Zeike
PB - Springer Verlag
Y2 - 20 September 2018 through 20 September 2018
ER -