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Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

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Abstract

Recently, there have been great interests for computer-aided diagnosis of Alzheimer’s disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous methods that considered simple low-level features such as gray matter tissue volumes from MRI, and mean signal intensities from PET, in this paper, we propose a deep learning-based latent feature representation with a stacked auto-encoder (SAE). We believe that there exist latent non-linear complicated patterns inherent in the low-level features such as relations among features. Combining the latent information with the original features helps build a robust model in AD/MCI classification, with high diagnostic accuracy. Furthermore, thanks to the unsupervised characteristic of the pre-training in deep learning, we can benefit from the target-unrelated samples to initialize parameters of SAE, thus finding optimal parameters in fine-tuning with the target-related samples, and further enhancing the classification performances across four binary classification problems: AD vs. healthy normal control (HC), MCI vs. HC, AD vs. MCI, and MCI converter (MCI-C) vs. MCI non-converter (MCI-NC). In our experiments on ADNI dataset, we validated the effectiveness of the proposed method, showing the accuracies of 98.8, 90.7, 83.7, and 83.3 % for AD/HC, MCI/HC, AD/MCI, and MCI-C/MCI-NC classification, respectively. We believe that deep learning can shed new light on the neuroimaging data analysis, and our work presented the applicability of this method to brain disease diagnosis.

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Notes

  1. URL: http://www.loni.ucla.edu/ADNI.

  2. Although there exist in total more than 800 subjects in ADNI database, only 202 subjects have the baseline data including all the modalities of MRI, FDG-PET, and CSF.

  3. Refer to http://www.adniinfo.org for the details.

  4. URL: http://mipav.cit.nih.gov/clickwrap.php.

  5. URL: http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/.

  6. While the low-level simple features should be the voxels in MRI and FDG-PET, due to high dimensionality and a small sample problem, in this paper, we take a ROI-based approach and consider the conical GM tissue volumes and the mean intensity for each ROI from MRI and FDG-PET, respectively, as the low-level features.

  7. In this work, we set γ = 0.01 and ρ = 0.05.

  8. In our case, the tasks are to regress class-label, and MMSE and ADAS-Cog scores.

  9. In this work, \({\user2 t}^{(1)}_{s}=\cdots={\user2 t}^{(m)}_{s}=\cdots={\user2 t}^{(M)}_{s}.\)

  10. URL: http://www.public.asu.edu/~jye02/Software/SLEP/index.htm.

  11. CONCAT represents a concatenation of the features from MRI, FDG-PET, and CSF into a single vector, which is the most direct and intuitive way of combining multimodal information.

  12. We considered [100, 300, 500, 1,000]–[50, 100]–[10, 20, 30] and [10, 20, 30]–[1, 2, 3] (bottom–top) for three-layer and two-layer networks, respectively.

  13. Refer to "Sparse auto-encoder" for explanation of the supervised learning.

  14. URL: https://github.com/rasmusbergpalm/DeepLearnToolbox.

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Acknowledgments

This work was supported in part by NIH grants EB006733, EB008374, EB009634, AG041721, MH100217, and AG042599, and also by the National Research Foundation grant (No. 2012-005741) funded by the Korean government.

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  1. Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina, Chapel Hill, NC, 27599, USA

    Heung-Il Suk & Dinggang Shen

  2. Department of Brain and Cognitive Engineering, Korea University, Seoul, 136-713, Republic of Korea

    Seong-Whan Lee & Dinggang Shen

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Suk, HI., Lee, SW., Shen, D. et al. Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct Funct 220, 841–859 (2015). https://doi.org/10.1007/s00429-013-0687-3

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