Abstract
In this chapter, we introduce our recent work on neuroimaging-based AD diagnosis with machine learning techniques, especially deep learning. Specifically, we focus on the problems of feature representation and complementary information fusion from different modalities, e.g., MRI and PET. In our experimental results on the publicly available ADNI dataset, we could validate the effectiveness of the deep learning-based feature representation and its superiority to the competing methods. We also present the importance of collaborating communities of machine learning and clinical neuroscience for clinical interpretation of the learned feature representations.
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- 1.
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://www.loni.ucla.edu/ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete list of ADNI investigators is available at http://adni.loni.ucla.edu/wpcontent/uploads/how_to_apply/ADNI_Authorship_List.pdf.
- 2.
In our work, ‘progressive’ and ‘stable’ denote whether the subjects with MCI progressed to AD in 18 months.
- 3.
The number of hidden units were manually determined proportional to the input dimension. As for the sparsity target and the weighting parameter of the sparsity penalty in Eq. (14.1), we set to ρ = 0. 05 and γ = 0. 01.
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Acknowledgements
This chapter follows closely the prior published papers [30, 31] by the authors. This work was supported in part by NIH grants EB006733, EB008374, EB009634, AG041721, MH100217, and AG042599, and partial supported by ICT R&D program of MSIP/IITP [B0101-15-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)].
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Suk, HI., Shen, D., Alzheimer’s Disease Neuroimaging Initiative. (2015). Deep Learning in Diagnosis of Brain Disorders. In: Lee, SW., Bülthoff, H., Müller, KR. (eds) Recent Progress in Brain and Cognitive Engineering. Trends in Augmentation of Human Performance, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7239-6_14
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