Abstract
Clinicians implicitly incorporate the complementarity of multi-modal data for disease diagnosis. Often a varied order of importance for this heterogeneous data is considered for personalized decisions. Current learning-based methods have achieved better performance with uniform attention to individual information, but a very few have focused on patient-specific attention learning schemes for each modality. Towards this, we introduce a model which not only improves the disease prediction but also focuses on learning patient-specific order of importance for multi-modal data elements. In order to achieve this, we take advantage of LSTM-based attention mechanism and graph convolutional networks (GCNs) to design our model. GCNs learn multi-modal but class-specific features from the entire population of patients, whereas the attention mechanism optimally fuses these multi-modal features into a final decision, separately for each patient. In this paper, we apply the proposed approach for disease prediction task for Parkinson’s and Alzheimer’s using two public medical datasets.
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
Bu, L.-L., et al.: Toward precision medicine in parkinson’s disease. Annal. Transl. Med. 4(2), 26 (2016)
Peng, X., et al.: Towards personalized intervention for Alzheimer’s disease. Genomics, Proteomics Bioinform. 14(5), 289–297 (2016)
Ng, K., Sun, J., Hu, J., Wang, F.: Personalized predictive modeling and risk factor identification using patient similarity. Am. Med. Inform. Assoc. Summits Transl. Sci. Proc. 2015, 132 (2015)
Suo, Q., et al.: Personalized disease prediction using a CNN-based similarity learning method. In: International Conference on Bioinformatics and Biomedicine 2017, pp. 811–816, IEEE (2017)
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
Kazi, A., Shekarforoush, S., Kortuem, K., Albarqouni, S., Navab, N.: Self-attention equipped graph convolutions for disease prediction. arXiv preprint arXiv:1812.09954 (2018)
Zhang, X., He, L., Chen, K., Luo, Y., Zhou, J., Wang, F.: Multi-view graph convolutional network and its applications on neuroimage analysis for parkinson’s disease. arXiv preprint arXiv:1805.08801 (2018)
Ma, T., Xiao, C., Zhou, J., Wang, F.: Drug similarity integration through attentive multi-view graph auto-encoders. In: International Joint Conferences on Artificial Intelligence (2018)
Ma, Y., Wang, S., Aggarwal, C.C., Yin, D., Tang, J.: Multi-dimensional graph convolutional networks. In: Social and Information Networks, pp. 657–665 (2018)
Veličković, P., Cucurull, P., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)
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
Fout, A., Byrd, J., Shariat, B., Ben-Hur, A.: Protein interface prediction using graph convolutional networks. In: Neural Information Processing Systems, pp. 6530–6539 (2017)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Neural Information Processing Systems, pp. 3844–3852 (2016)
Marinescu, R.V., et al.: Tadpole challenge: prediction of longitudinal evolution in Alzheimer’s disease. arXiv preprint arXiv:1805.03909 (2018)
Baur, C., Wiestler, B., Albarqouni, S., Navab, N.: Deep autoencoding models for unsupervised anomaly segmentation in brain MR images. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 161–169. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11723-8_16
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017)
Acknowledgement
The study was carried out with financial support of Freunde und Förderer der Augenklinik, München, Germany, Carl Zeiss Meditec AG, Germany and the German Federal Ministry of Education and Research (BMBF) in connection with the foundation of the German Center for Vertigo and Balance Disorders (DSGZ) (grant number 01 EO 0901).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kazi, A. et al. (2019). Graph Convolution Based Attention Model for Personalized Disease Prediction. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-32251-9_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32250-2
Online ISBN: 978-3-030-32251-9
eBook Packages: Computer ScienceComputer Science (R0)
