Ensemble Sparse Classification of Alzheimer’s Disease

By Manhua Liu, Daoqiang Zhang, Dinggang Shen. NeuroImage, 2012

322 citations recorded by the source corpus.

The high-dimensional pattern classification methods, e.g., support vector machines (SVM), have been widely investigated for analysis of structural and functional brain images (such as magnetic resonance imaging (MRI)) to assist the diagnosis of Alzheimer’s disease (AD) including its prodromal stage, i.e., mild cognitive impairment (MCI). Most existing classification methods extract features from neuroimaging data and then construct a single classifier to perform classification. However, due to noise and small sample size of neuroimaging data, it is challenging to train only a global classifier that can be robust enough to achieve good classification performance. In this paper, instead of building a single global classifier, we propose a local patch-based subspace ensemble method which builds multiple individual classifiers based on different subsets of local patches and then combines them for more accurate and robust classification. Specifically, to capture the local spatial consistency, each brain image is partitioned into a number of local patches and a subset of patches is randomly selected from the patch pool to build a weak classifier. Here, the sparse representation-based classification (SRC) method, which has shown effective for classification of image data (e.g., face), is used to construct each weak classifier. Then, multiple weak classifiers are combined to make the final decision. We evaluate our method on 652 subjects (including 198 AD patients, 225 MCI and 229 normal controls) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using MR images. The experimental results show that our method achieves an accuracy of 90.8% and an area under the ROC curve (AUC) of 94.86% for AD classification and an accuracy of 87.85% and an AUC of 92.90% for MCI classification, respectively, demonstrating a very promising performance of our method compared with the state-of-the-art methods for AD/MCI classification using MR images.

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

View Paper: https://www.semanticscholar.org/paper/f8edcbe5005384d738507a5367e96cfeda987f14

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Citations in the corpus

Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness (2014, background): “A large number of studies addressing the use of pattern recognition methods in image-based detection of AD have been published in recent years (Gray et al., 2013; Liu et al., 2012; Cuingnet et al., 2011; Klöppel et al., 2008).”
Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks (2018, unknown): “The layers in the dense blocks of M 5 have the arrangement 6, 12, 64, 48 as shown in Fig.”
Latent feature representation with stacked auto-encoder for AD/MCI diagnosis (2013, methodology): “In this work, we only considered GM for classification, because of its relatively high relatedness to AD/MCI compared to WM and CSF (Liu et al. 2012).”
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis (2014, unknown): “To tackle these limitations, recently, Liu et al. proposed a patch-basedmethod that first dissected brain areas into small 3D patches, extracted features from each selected patch individually, and then combined the features hierarchically in a classifier level (Liu et al., 2012, 2013).”
Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease (2018, unknown): “[21] M.”
Deep ensemble learning of sparse regression models for brain disease diagnosis (2017, background): “In this work, we considered only the spatially normalized GM densities, due to its relatively high relevance to AD compared to WM and CSF ( Liu et al., 2012 ).”