The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
Latent feature representation with stacked auto-encoder for AD/MCI diagnosis
Open accessAbstract
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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Metadata
- Authors
- Initiative, T. A. D. N., Suk, H.-I., Lee, S.-W., & Shen, D.
- Venue
- Brain Structure and Function
- Published
- 2013-12-22
- Volume
- 220
- Issue
- 2
- Pages
- 841-859
- ISSN
- 1863-2653 (ISSN-L)
- 1863-2661
- OpenAlex
- W1974874858
- Cited by
- 526
- References
- 89
- FWCI
- 7.44
- Type
- journal article
- Language
- en
- Open access
- green
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- Initiative, T. A. D. N., Suk, H.-I., Lee, S.-W., & Shen, D. (2015). Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Structure and Function. https://doi.org/10.1007/s00429-013-0687-3
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