Abstract
Utilization of biomedical data from multiple modalities improves the diagnostic accuracy of neurodegenerative diseases. However, multi-modality data are often incomplete because not all data can be collected for every individual. When using such incomplete data for diagnosis, current approaches for addressing the problem of missing data, such as imputation, matrix completion and multi-task learning, implicitly assume linear data-to-label relationship, therefore limiting their performances. We thus propose multi-task deep learning for incomplete data, where prediction tasks that are associated with different modality combinations are learnt jointly to improve the performance of each task. Specifically, we devise a multi-input multi-output deep learning framework, and train our deep network subnet-wise, partially updating its weights based on the availability of modality data. The experimental results using the ADNI dataset show that our method outperforms the state-of-the-art methods.
This work was supported in part by NIH grants NS093842, EB006733, EB008374, EB009634, EB022880, AG041721, MH100217, and AG042599.
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References
Adeli-Mosabbeb, E., et al.: Robust feature-sample linear discriminant analysis for brain disorders diagnosis. In: Advances in Neural Information Processing Systems, pp. 658–666 (2015)
Candès, E.J., et al.: Exact matrix completion via convex optimization. Found. Comput. Math. 9(6), 717–772 (2009)
Chollet, F.: Keras (2015). https://github.com/fchollet/keras
Goldberg, A., et al.: Transduction with matrix completion: three birds with one stone. Adv. Neural Inf. Process. Syst. 23, 757–765 (2010)
Li, F., et al.: A robust deep model for improved classification of AD/MCI patients. IEEE J. Biomed. Health Inf. 19(5), 1610–1616 (2015)
Liu, S., et al.: Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015)
Thung, K.H., et al.: Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion. Neuroimage 91, 386–400 (2014)
Thung, K.H., et al.: Identification of progressive mild cognitive impairment patients using incomplete longitudinal MRI scans. Brain Struct. Funct. 221(8), 3979–3995 (2016)
Thung, K.-H., Yap, P.-T., Adeli-M, E., Shen, D.: Joint diagnosis and conversion time prediction of progressive mild cognitive impairment (pMCI) using low-rank subspace clustering and matrix completion. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 527–534. Springer, Cham (2015). doi:10.1007/978-3-319-24574-4_63
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). doi:10.1007/978-3-319-46723-8_11
Troyanskaya, O., et al.: Missing value estimation methods for DNA microarrays. Bioinformatics 17(6), 520–525 (2001)
Wang, Y., Nie, J., Yap, P.-T., Shi, F., Guo, L., Shen, D.: Robust deformable-surface-based skull-stripping for large-scale studies. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6893, pp. 635–642. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23626-6_78
Weiner, M.W., et al.: The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimer’s Dementia 9(5), e111–e194 (2013)
Wyman, B.T., et al.: Standardization of analysis sets for reporting results from ADNI MRI data. Alzheimer’s Dementia 9(3), 332–337 (2013)
Yu, G., et al.: Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals. PLoS ONE 9(5), e96458 (2014)
Yuan, L., et al.: Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data. NeuroImage 61(3), 622–632 (2012)
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Thung, KH., Yap, PT., Shen, D. (2017). Multi-stage Diagnosis of Alzheimer’s Disease with Incomplete Multimodal Data via Multi-task Deep Learning. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_19
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