Abstract
Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful machine learning tool for Computer Aided Diagnosis (CADx) and disease prediction. A key component in these models is to build a population graph, where the graph adjacency matrix represents pair-wise patient similarities. Until now, the similarity metrics have been defined manually, usually based on meta-features like demographics or clinical scores. The definition of the metric, however, needs careful tuning, as GCNs are very sensitive to the graph structure. In this paper, we demonstrate for the first time in the CADx domain that it is possible to learn a single, optimal graph towards the GCN’s downstream task of disease classification. To this end, we propose a novel, end-to-end trainable graph learning architecture for dynamic and localized graph pruning. Unlike commonly employed spectral GCN approaches, our GCN is spatial and inductive, and can thus infer previously unseen patients as well. We demonstrate significant classification improvements with our learned graph on two CADx problems in medicine. We further explain and visualize this result using an artificial dataset, underlining the importance of graph learning for more accurate and robust inference with GCNs in medical applications.
L. Cosmo, A. Kazi—Equal contribution
N. Navab, M. Bronstein—Shared last authorship.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond Euclidean data. IEEE Sig. Process. Mag. 34(4), 18–42 (2017)
Burwinkel, H., et al.: Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 640–648. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_71
Chamberlain, B.P., Clough, J., Deisenroth, M.P.: Neural embeddings of graphs in hyperbolic space. arXiv preprint arXiv:1705.10359 (2017)
Fischl, B.: FreeSurfer. NeuroImage 62(2), 774–781 (2012)
Franceschi, L., Niepert, M., Pontil, M., He, X.: Learning discrete structures for graph neural networks. In: Proceedings of International Conference Machine Learning (ICML). Proceedings of Machine Learning Research, vol. 97, pp. 1972–1982. PMLR (2019)
Gainza, P., Sverrisson, F., Monti, F., Rodola, E., Bronstein, M.M., Correia, B.E.: Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nat. Methods 17, 184–192 (2019)
Granitto, P.M., Furlanello, C., Biasioli, F., Gasperi, F.: Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometr. Intell. Lab. Syst. 83(2), 83–90 (2006)
Jang, S., Moon, S., Lee, J.: Brain signal classification via learning connectivity structure. arXiv abs/1905.11678 (2019)
Kazi, A., Cosmo, L., Navab, N., Bronstein, M.: Differentiable graph module (DGM) graph convolutional networks. arXiv preprint arXiv:2002.04999 (2020)
Kazi, A., Krishna, S., Shekarforoush, S., Kortuem, K., Albarqouni, S., Navab, N.: Self-attention equipped graph convolutions for disease prediction. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1896–1899 (2019)
Kazi, A., et al.: InceptionGCN: receptive field aware graph convolutional network for disease prediction. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 73–85. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_6
Kazi, A., et al.: Graph convolution based attention model for personalized disease prediction. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 122–130. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_14
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Ktena, S., et al.: Metric learning with spectral graph convolutions on brain connectivity networks. NeuroImage 169, 431 (2018)
Marinescu, R., et al.: Tadpole challenge: prediction of longitudinal evolution in Alzheimer’s disease. arXiv preprint arXiv:1805.03909 (2018)
Miller, K.L., et al.: Multimodal population brain imaging in the UK biobank prospective epidemiological study. Nat. Neurosci 19(11), 1523 (2016)
Ortega, A., Frossard, P., Kovacevic, J., Moura, J.M.F., Vandergheynst, P.: Graph signal processing: overview, challenges, and applications. Proc. IEEE 106(5), 808–828 (2018)
Parisot, S., Ktena, S.I., Ferrante, E., Lee, M., Guerrero, R., Glocker, B., Rueckert, D.: Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer’s disease. Med. Image Anal. 48, 117–130 (2018)
Parisot, S., et al.: Spectral graph convolutions for population-based disease prediction. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 177–185. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_21
Qiao, L., Zhang, L., Chen, S., Shen, D.: Data-driven graph construction and graph learning: a review. Neurocomputing 312, 336–351 (2018)
Valenchon, J., Coates, M.: Multiple-graph recurrent graph convolutional neural network architectures for predicting disease outcomes. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3157–3161. IEEE (2019)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv:1710.10903 (2017)
Vivar, G., Zwergal, A., Navab, N., Ahmadi, S.-A.: Multi-modal disease classification in incomplete datasets using geometric matrix completion. In: Stoyanov, D., et al. (eds.) GRAIL/Beyond MIC -2018. LNCS, vol. 11044, pp. 24–31. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00689-1_3
Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM TOG 38(5), 146 (2019)
Zhan, K., Chang, X., Guan, J., Chen, L., Ma, Z., Yang, Y.: Adaptive structure discovery for multimedia analysis using multiple features. IEEE Trans. Cybern. 49(5), 1826–1834 (2019)
Acknowledgement
The study was carried out with financial support of TUM-ICL incentive funding, Freunde und Förderer der Augenklinik, München, Germany and ERC Consolidator grant No. 724228 (LEMAN) and German Federal Ministry of Education and Health (BMBF) in connection with the foundation of the German Center for Vertigo and Balance Disorders (DSGZ) [grant number 01 EO0901]. The UK Biobank data is used under the application id 51541.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Cosmo, L., Kazi, A., Ahmadi, SA., Navab, N., Bronstein, M. (2020). Latent-Graph Learning for Disease Prediction. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_62
Download citation
DOI: https://doi.org/10.1007/978-3-030-59713-9_62
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59712-2
Online ISBN: 978-3-030-59713-9
eBook Packages: Computer ScienceComputer Science (R0)
