Multi-modal classification of Alzheimer's disease using nonlinear graph fusion

Tong, Tong; Gray, Katherine R.; Gao, Qinquan; Chen, Liang; Rueckert, Daniel

doi:10.1016/j.patcog.2016.10.009

Cited by 203 publications

(102 citation statements)

References 56 publications

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“…The model then learns the relative contribution of the different sources of information for prediction. This direction has been investigated in a number of studies for diagnosis (54)(55)(56)(57)(58)(59)(60)(61) and to a lesser extent for prognosis (33,58,(62)(63)(64).…”

Section: Advanced Machine Learning Techniques What Do They Offer?mentioning

confidence: 99%

Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning

Janssen

Mourão-Miranda

Schnack

2018

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

122

103

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Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the future, as opposed to making a diagnosis, which is concerned with the current state. During the follow-up period, many factors will influence the course of the disease. Combined with the usually scarcer longitudinal data and the variability in the definition of outcomes/transition, this makes prognostic predictions a challenging endeavor. Employing neuroimaging data in this endeavor introduces the additional hurdle of high dimensionality. Machine learning techniques are especially suited to tackle this challenging problem. This review starts with a brief introduction to machine learning in the context of its application to clinical neuroimaging data. We highlight a few issues that are especially relevant for prediction of outcome and transition using neuroimaging. We then review the literature that discusses the application of machine learning for this purpose. Critical examination of the studies and their results with respect to the relevant issues revealed the following: 1) there is growing evidence for the prognostic capability of machine learning-based models using neuroimaging; and 2) reported accuracies may be too optimistic owing to small sample sizes and the lack of independent test samples. Finally, we discuss options to improve the reliability of (prognostic) prediction models. These include new methodologies and multimodal modeling. Paramount, however, is our conclusion that future work will need to provide properly (cross-)validated accuracy estimates of models trained on sufficiently large datasets. Nevertheless, with the technological advances enabling acquisition of large databases of patients and healthy subjects, machine learning represents a powerful tool in the search for psychiatric biomarkers.

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Section: Advanced Machine Learning Techniques What Do They Offer?mentioning

confidence: 99%

Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning

Janssen

Mourão-Miranda

Schnack

2018

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

122

103

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“…T. Tong et al, [14] presented a multi-modality classification system for exploiting complementarity in the multi-modal data. Initially, a pairwise similarity was used to determine the modality of features like CSF biomarker measures, categorical genetic information, regional MRI volumes, and voxel based signal intensities.…”

Section: Literature Reviewmentioning

confidence: 99%

“…: f = √|| || 2 2 + 2 (14) Where, is denoted as small positive value, is represented as feature extracted value, is meant as non-normalized vector in histogram blocks and || || 2 2 represents the 2-norm of HOG normalization.…”

Section: −mentioning

confidence: 99%

Relieff Feature Selection Based Alzheimer Disease Classification Using Hybrid Features and Support Vector Machine in Magnetic Resonance Imaging

Suresha¹,

Parthasarathi²

2019

IJCET

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Alzheimer disease is a form of dementia that results in memory-related problems in human beings. An accurate detection and classification of Alzheimer disease and its stages plays a crucial role in human health monitoring system. In this research paper, Alzheimer disease classification was assessed by Alzheimer's disease Neuro-Imaging Initiative (ADNI) dataset. After performing histogram equalization and skull removal of the collected brain images, segmentation was carried-out using Fuzzy C-Means (FCM) for segmenting the white matter, Cerebro-Spinal Fluid (CSF), and grey matter from the pre-processed brain images. Then, hybrid feature extraction (Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and Gray-Level Co-Occurrence Matrix (GLCM)) was performed for extracting the feature values from the segmented brain images. After hybrid feature extraction, reliefF feature selection was used for selecting the optimal feature subsets or to reject the irrelevant feature vectors. Then, the selected optimal feature vectors were given as the input to a supervised classifier Support Vector Machine (SVM) to classify three Alzheimer classes of subjects; those are normal, Alzheimer disease and Mild Cognitive Impairment (MCI). The experimental outcome showed that the proposed methodology performed effectively by means of sensitivity, accuracy, specificity, and f-score. The proposed methodology enhanced the classification accuracy up to 2-20% compared to the existing methodologies.

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“…LP has different applications in community detection [16], image segmentation [17], clustering [18], and classification [19] tasks. Although most of the algorithms use one single graph as an input of the LP algorithms, such as the Zhou method [20], flexible manifold embedding (FME) [21], local and global consistency (LGC) [22], and Gaussian fields and harmonic function (GFHF) [23], exploiting various similarity graphs can enhance the performance of LP process (graph-fusion methods) [15,[24][25][26][27][28], thereby creating multiple similarity graphs where each contains complementary information of data and fusing them together can lead to a better representation of data.…”

Section: Introductionmentioning

confidence: 99%

Multi Similarity Metric Fusion in Graph-Based Semi-Supervised Learning

2019

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In semi-supervised label propagation (LP), the data manifold is approximated by a graph, which is considered as a similarity metric. Graph estimation is a crucial task, as it affects the further processes applied on the graph (e.g., LP, classification). As our knowledge of data is limited, a single approximation cannot easily find the appropriate graph, so in line with this, multiple graphs are constructed. Recently, multi-metric fusion techniques have been used to construct more accurate graphs which better represent the data manifold and, hence, improve the performance of LP. However, most of these algorithms disregard use of the information of label space in the LP process. In this article, we propose a new multi-metric graph-fusion method, based on the Flexible Manifold Embedding algorithm. Our proposed method represents a unified framework that merges two phases: graph fusion and LP. Based on one available view, different simple graphs were efficiently generated and used as input to our proposed fusion approach. Moreover, our method incorporated the label space information as a new form of graph, namely the Correlation Graph, with other similarity graphs. Furthermore, it updated the correlation graph to find a better representation of the data manifold. Our experimental results on four face datasets in face recognition demonstrated the superiority of the proposed method compared to other state-of-the-art algorithms.

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Multi-modal classification of Alzheimer's disease using nonlinear graph fusion

Cited by 203 publications

References 56 publications

Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning

Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning

Relieff Feature Selection Based Alzheimer Disease Classification Using Hybrid Features and Support Vector Machine in Magnetic Resonance Imaging

Multi Similarity Metric Fusion in Graph-Based Semi-Supervised Learning

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