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Erschienen in: Brain Structure and Function 2/2015

01.03.2015 | Original Article

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

verfasst von: Heung-Il Suk, Seong-Whan Lee, Dinggang Shen, The Alzheimer’s Disease Neuroimaging Initiative

Erschienen in: Brain Structure and Function | Ausgabe 2/2015

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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.
Fußnoten
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.
 
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, \({\user2 t}^{(1)}_{s}=\cdots={\user2 t}^{(m)}_{s}=\cdots={\user2 t}^{(M)}_{s}.\)
 
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.
 
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Metadaten
Titel
Latent feature representation with stacked auto-encoder for AD/MCI diagnosis
verfasst von
Heung-Il Suk
Seong-Whan Lee
Dinggang Shen
The Alzheimer’s Disease Neuroimaging Initiative
Publikationsdatum
01.03.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Brain Structure and Function / Ausgabe 2/2015
Print ISSN: 1863-2653
Elektronische ISSN: 1863-2661
DOI
https://doi.org/10.1007/s00429-013-0687-3

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