Graph saliency maps through spectral convolutional networks: Application to sex classification with brain connectivity

Arslan S, Ktena SI, Glocker B, Rueckert D (2018)


Publication Type: Conference contribution

Publication year: 2018

Journal

Publisher: Springer Verlag

Book Volume: 11044 LNCS

Pages Range: 3-13

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Granada, ESP

ISBN: 9783030006884

DOI: 10.1007/978-3-030-00689-1_1

Abstract

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.

Involved external institutions

How to cite

APA:

Arslan, S., Ktena, S.I., Glocker, B., & Rueckert, D. (2018). Graph saliency maps through spectral convolutional networks: Application to sex classification with brain connectivity. In Danail Stoyanov, Aristeidis Sotiras, Bartlomiej Papiez, Adrian V. Dalca, Anne Martel, Sarah Parisot, Enzo Ferrante, Lena Maier-Hein, Mert R. Sabuncu, Li Shen, Zeike Taylor (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 3-13). Granada, ESP: Springer Verlag.

MLA:

Arslan, Salim, et al. "Graph saliency maps through spectral convolutional networks: Application to sex classification with brain connectivity." Proceedings of the 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, Granada, ESP Ed. Danail Stoyanov, Aristeidis Sotiras, Bartlomiej Papiez, Adrian V. Dalca, Anne Martel, Sarah Parisot, Enzo Ferrante, Lena Maier-Hein, Mert R. Sabuncu, Li Shen, Zeike Taylor, Springer Verlag, 2018. 3-13.

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