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

By Heung-Il Suk, Seong-Whan Lee, Dinggang Shen. Brain Structure and Function, 2013

516 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Neurology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/0a1e8435516b9da400cee8c4a70e9c6a236220b5

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Citations in the corpus

Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks (2018, methodology): “Suk et al. [23, 42, 78, 79] developed an autoencoder network-based model for AD diagnosis and used several complex SVM kernels for classification.”
State-space model with deep learning for functional dynamics estimation in resting-state fMRI (2016, unknown): “In these regards, the analysis of functional characteristics inherent in rs-fMRI is playing a core role for brain disease diagnosis or prognosis (Handwerker et al., 2012; Li et al., 2012; Leonardi et al., 2013; Hjelm et al., 2014; Wee et al., 2014; Suk et al., 2015c).”
Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans (2016, methodology): “…to the medical image analysis domain with promising results on various applications, like the computerized prognosis for Alzheimer’s disease and mild cognitive impairment 32 , organ segmentations 33 and detection 34 , ultrasound standard plane selection 35 , etc., on 3D or 4D image data, etc.”
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia (2015, unknown): “Despite accumulating evidence showing the superiority of the DNN, previous applications of DNN to neuroimaging data are limited to only a few studies (Brosch and Tam, 2013; Hjelm et al., 2014; Plis et al., 2014; Suk et al., 2013).”
DeepAD: Alzheimer’s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI (2016, methodology): “[24] [25] [26] developed a deep learning-based method to classify AD magnetic current imaging (MCI) and MCI-converter structural MRI and PET data, achieving accuracy rates of 95.”
Deep ensemble learning of sparse regression models for brain disease diagnosis (2017, methodology): “…warping the atlas of Kabani et al. (1998) , which has been widely used for AD/MCI diagnosis studies ( Davatzikos et al., 2011; Negash et al., 2013; Suk et al., 2015a ) to each subject’s space via HAMMER ( Shen and Davatzikos, 2002 ), although many other advanced registration methods could be also…”
Representation learning for mammography mass lesion classification with convolutional neural networks (2016, unknown): “[13] H.”
Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications (2017, unknown): “Due to the multiple nonlinearities, it can be challenging to trace the consecutive layers of weights back to the original brain image in order to identify which features (e. g. regions) are providing the greatest contribution to classiﬁcation (Suk et al., 2015a .”
Deep Learning in Medical Image Analysis (2017, methodology): “…inspired its use to medical image analysis such as image segmentation (22, 23), image registration (24), image fusion (25), image annotation (26), computer-aided diagnosis and prognosis (27, 28, 29), lesion/landmark detection (30, 31, 32), and microscopic imaging analysis (33, 34), to name a few.”
A deep learning framework for supporting the classification of breast lesions in ultrasound images (2017, background): “diagnosis for Alzheimers disease[46, 47, 48, 49] and mild cognitive impairment[47], 78”
Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI (2018, background): “…fcMRI classi ﬁ cation of schizophrenia patients vs. healthy controls with a small sample size ( n = 100) (Kim et al., 2016), illuminating the potential of deep learning in automatic diagnosis of clinical populations(Hazlett et al., 2017; Kawahara et al., 2017; Suk et al., 2013; Zhao et al., 2017).”