Abstract
Learning complementary information from multi-modality data often improves diagnostic performance of brain disorders. However, it is challenging to obtain this complementary information when the data are incomplete. Existing methods, such as low-rank matrix completion (which imputes the missing data) and multi-task learning (which restructures the problem into the joint learning of multiple tasks, with each task associated with a subset of complete data), simply concatenate features from different modalities without considering their underlying correlations. Furthermore, most methods conduct multi-modality fusion and prediction model learning in separated steps, which may render to a sub-optimal solution. To address these issues, we propose a novel diagnostic model that integrates missing data recovery, latent space learning and prediction model learning into a unified framework. Specifically, we first recover the missing modality by maximizing the dependency among different modalities. Then, we further exploit the modality correlation by projecting different modalities into a common latent space. Besides, we employ an \(\ell _1\)-norm to our loss function to mitigate the influence of sample outliers. Finally, we map the learned latent representation into the label space. All these tasks are learned iteratively in a unified framework, where the label information (from the training samples) can also inherently guide the missing modality recovery. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show the effectiveness of our method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Bold capital letters denotes matrices (e.g., \(\mathbf {X}\)), bold small letters denote vectors (e.g., \(\mathbf {x}\)), and non-bold letters denote scalar variables (e.g., \(d_1\)). \(\Vert \cdot \Vert _F\), \(\Vert \cdot \Vert _1\), and \(\Vert \cdot \Vert _{2,1}\) denote the F, \(\ell _1\), and \(\ell _{2,1}\) norms, respectively. Besides, \(\mathbf {X}^{\top }\) denotes the transpose operator of a matrix \(\mathbf {X}\).
References
Gretton, A., Bousquet, O., Smola, A., Schölkopf, B.: Measuring statistical dependence with Hilbert-Schmidt norms. In: Jain, S., Simon, H.U., Tomita, E. (eds.) ALT 2005. LNCS (LNAI), vol. 3734, pp. 63–77. Springer, Heidelberg (2005). https://doi.org/10.1007/11564089_7
Guo, J., Zhu, W.: Partial multi-view outlier detection based on collective learning. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Hu, M., Chen, S.: Doubly aligned incomplete multi-view clustering. In: IJCAI (2018)
Lin, Z., Liu, R., Su, Z.: Linearized alternating direction method with adaptive penalty for low-rank representation. In: NIPS, pp. 612–620 (2011)
Liu, M., Gao, Y., Yap, P.T., Shen, D.: Multi-hypergraph learning for incomplete multimodality data. IEEE J. Biomed. Health Inf. 22(4), 1197–1208 (2018)
Misra, C., et al.: Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. NeuroImage 44(4), 1415–1422 (2009)
Ritter, K., et al.: Multimodal prediction of conversion to Alzheimer’s disease based on incomplete biomarkers. Alzheimer’s Dement. Diagn. Assess. Dis. Monit. 1(2), 206–215 (2015)
Shi, J., et al.: Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer’s disease. IEEE JBHI 22(1), 173–183 (2018)
Tan, Q., et al.: Incomplete multi-view weak-label learning. In: IJCAI, pp. 2703–2709 (2018)
Thung, K.-H., Adeli, E., Yap, P.-T., Shen, D.: Stability-weighted matrix completion of incomplete multi-modal data for disease diagnosis. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 88–96. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_11
Thung, K.H., et al.: Conversion and time-to-conversion predictions of mild cognitive impairment using low-rank affinity pursuit denoising and matrix completion. Med. Image Anal. 45, 68–82 (2018)
Tong, T., Gray, K., Gao, Q., Chen, L., Rueckert, D.: Multi-modal classification of Alzheimer’s disease using nonlinear graph fusion. Pattern Recogn. 63, 171–181 (2017)
Troyanskaya, O., Cantor, M., et al.: Missing value estimation methods for DNA microarrays. Bioinformatics 17(6), 520–525 (2001)
Wang, J., Wang, Q., Zhang, H., Chen, J., Wang, S., Shen, D.: Sparse multiview task-centralized ensemble learning for asd diagnosis based on age-and sex-related functional connectivity patterns. IEEE Trans. Cybern. 49(8), 3141–3154 (2018)
Wang, Z., Liu, C., Cheng, D., Wang, L., Yang, X., Cheng, K.T.: Automated detection of clinically significant prostate cancer in mp-MRI images based on an end-to-end deep neural network. IEEE Trans. Med. Imaging 37(5), 1127–1139 (2018)
Xiang, S., Yuan, L., Fan, W., Wang, Y., Thompson, P.M., Ye, J.: Bi-level multi-source learning for heterogeneous block-wise missing data. NeuroImage 102, 192–206 (2014)
Zhang, C., Hu, Q., et al.: Latent multi-view subspace clustering. In: IEEE CVPR, pp. 4279–4287 (2017)
Zhou, T., Liu, M., Thung, K.H., Shen, D.: Latent representation learning for Alzheimers disease diagnosis with incomplete multi-modality neuroimaging and genetic data. IEEE Trans. Med. Imaging (2019)
Zhou, T., Thung, K.H., Zhu, X., Shen, D.: Effective feature learning and fusion of multimodality data using stage wise deep neural network for dementia diagnosis. Hum. Brain Mapp. 40(3), 1001–1016 (2019)
Zhou, T., Thung, K.H., Liu, M., Shen, D.: Brain-wide genome-wide association study for Alzheimer’s disease via joint projection learning and sparse regression model. IEEE Trans. Biomed. Eng. 66(1), 165–175 (2018)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhou, T. et al. (2019). Inter-modality Dependence Induced Data Recovery for MCI Conversion 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_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-32251-9_21
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)