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D.: Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. NeuroImage 59
, 2012
"... Abstract. One recent interest in computer-aided diagnosis of neurological diseases is to predict the clinical scores from brain images. Most existing methods usually estimate multiple clinical variables separately, without considering the useful correlation information among them. On the other hand, ..."
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Abstract. One recent interest in computer-aided diagnosis of neurological diseases is to predict the clinical scores from brain images. Most existing methods usually estimate multiple clinical variables separately, without considering the useful correlation information among them. On the other hand, nearly all methods use only one modality of data (mostly structural MRI) for regression, and thus ignore the complementary information among different modalities. To address these issues, in this paper, we present a general methodology, namely Multi-Modal Multi-Task (M3T) learning, to jointly predict multiple variables from multi-modal data. Our method contains three major subsequent steps: (1) a multi-task feature selection which selects the common subset of relevant features for the related multiple clinical variables from each modality; (2) a kernel-based multimodal data fusion which fuses the above-selected features from all modalities; (3) a support vector regression which predicts multiple clinical variables based on the previously learnt mixed kernel. Experimental results on ADNI dataset with both imaging modalities (MRI and PET) and biological modality (CSF) validate the efficacy of the proposed M3T learning method. 1
Multi-Source Learning for Joint Analysis of Incomplete Multi-Modality Neuroimaging Data
"... Incomplete data present serious problems when integrating largescale brain imaging data sets from different imaging modalities. In the Alzheimer’s Disease Neuroimaging Initiative (ADNI), for example, over half of the subjects lack cerebrospinal fluid (CSF) measurements; an independent half of the su ..."
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Incomplete data present serious problems when integrating largescale brain imaging data sets from different imaging modalities. In the Alzheimer’s Disease Neuroimaging Initiative (ADNI), for example, over half of the subjects lack cerebrospinal fluid (CSF) measurements; an independent half of the subjects do not have fluorodeoxyglucose positron emission tomography (FDG-PET) scans; many lack proteomics measurements. Traditionally, subjects with missing measures are discarded, resulting in a severe loss of available information. We address this problem by proposing two novel learning methods where all the samples (with at least one available data source) can be used. In the first method, we divide our samples according to the availability of data sources, and we learn shared sets of features with state-of-the-art sparse learning methods. Our second method learns a base classifier for each data source independently, based on which we represent each source using a single column of prediction scores; we then estimate the missing prediction scores, which, combined with the existing prediction scores, are used to build a multi-source fusion model. To illustrate the proposed approaches, we classify patients from the ADNI study into groups with Alzheimer’s disease (AD), mild cognitive impairment (MCI) and normal controls, based on the multi-modality data. At baseline, ADNI’s 780 participants (172 AD, 397 MCI, 211 Normal), have at least one of four data types: magnetic resonance imaging (MRI), FDG-PET, CSF and proteomics. These data are used to test our algorithms. Comprehensive experiments show that our proposed methods yield stable and promising results.
Early Recognition and Disease Prediction in the At-RiskMental States for Psychosis Using Neurocognitive Pattern Classification
"... psychosis and overlap with the impairments in the estab-lished disease. However, to date, no single neurocognitive measure has shown sufficient power for a prognostic test. Thus, it remains to be determined whether multivariate neurocognitive pattern classification could facilitate the diagnostic id ..."
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psychosis and overlap with the impairments in the estab-lished disease. However, to date, no single neurocognitive measure has shown sufficient power for a prognostic test. Thus, it remains to be determined whether multivariate neurocognitive pattern classification could facilitate the diagnostic identification of different at-risk mental states (ARMS) for psychosis and the individualized prediction of illness transition. Methods: First, classification of 30 healthy controls (HC) vs 48 ARMS individuals subgrouped into 20 ‘‘early,’ ’ 28
Pattern analysis in neuroimaging: beyond two-class categorization
- Int. J. Imag. Syst. Technol
, 2011
"... ABSTRACT: One of the many advantages of multivariate pattern rec-ognition approaches over conventional mass-univariate group analy-sis using voxel-wise statistical tests is their potential to provide highly sensitive and specific markers of diseases on an individual basis. However, a vast majority o ..."
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ABSTRACT: One of the many advantages of multivariate pattern rec-ognition approaches over conventional mass-univariate group analy-sis using voxel-wise statistical tests is their potential to provide highly sensitive and specific markers of diseases on an individual basis. However, a vast majority of imaging problems addressed by pattern recognition are viewed from the perspective of a two-class classifica-tion. In this article, we provide a summary of selected works that pro-pose solutions to biomedical problems where the widely-accepted classification paradigm is not appropriate. These pattern recognition approaches address common challenges in many imaging studies: high heterogeneity of populations and continuous progression of dis-eases. We focus on diseases associated with aging and propose that clustering-based approaches may be more suitable for disentangle-ment of the underlying heterogeneity, while high-dimensional pattern regression methodology is appropriate for prediction of continuous and gradual clinical progression from magnetic resonance brain
NeuroImage 61 (2012) 622–632 Contents lists available at SciVerse ScienceDirect
"... journal homepage: www.elsevier.com/locate/ynimg Multi-source feature learning for joint analysis of incomplete multiple heterogeneous ..."
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journal homepage: www.elsevier.com/locate/ynimg Multi-source feature learning for joint analysis of incomplete multiple heterogeneous
Contents lists available at SciVerse ScienceDirect
"... journal homepage: www.elsevier.com/locate/ynimg 1 Multi-source feature learning for joint analysis of incomplete multiple heterogeneous ..."
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journal homepage: www.elsevier.com/locate/ynimg 1 Multi-source feature learning for joint analysis of incomplete multiple heterogeneous
NeuroImage 59 (2012) 895–907 Contents lists available at SciVerse ScienceDirect
"... journal homepage: www.elsevier.com/locate/ynimg Multi-modal multi-task learning for joint prediction of multiple regression and ..."
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journal homepage: www.elsevier.com/locate/ynimg Multi-modal multi-task learning for joint prediction of multiple regression and
Predicting Clinical Scores Using Semi-supervised Multimodal Relevance Vector Regression
"... Abstract. We present a novel semi-supervised multimodal relevance vector regression (SM-RVR) method for predicting clinical scores of neurological diseases from multimodal brain images, to help evaluate pathological stage and predict future progression of diseases, e.g., Alzheimer’s diseases (AD). D ..."
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Abstract. We present a novel semi-supervised multimodal relevance vector regression (SM-RVR) method for predicting clinical scores of neurological diseases from multimodal brain images, to help evaluate pathological stage and predict future progression of diseases, e.g., Alzheimer’s diseases (AD). Different from most existing methods, we predict clinical scores from multimodal (imaging and biological) biomarkers, including MRI, FDG-PET, and CSF. Also, since mild cognitive impairment (MCI) subjects generally contain more noises in their clinical scores compared to AD and healthy control (HC) subjects, we use only their multimodal data (i.e., MRI, FDG-PET and CSF), not their clinical scores, to train a semi-supervised model for enhancing the estimation of clinical scores for AD and healthy control (HC). Experimental results on ADNI dataset validate the efficacy of the proposed method. 1
Graduate Supervisory Committee:
"... Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of ℓ1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined s ..."
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Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of ℓ1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer’s Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables
Initiative 1
"... Many machine learning and pattern classification methods have been applied to the diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Recently, rather than predicting categorical variables as in classification, several pattern regression methods have ..."
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Many machine learning and pattern classification methods have been applied to the diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Recently, rather than predicting categorical variables as in classification, several pattern regression methods have also been used to estimate continuous clinical variables from brain images. However, most existing regression methods focus on estimating multiple clinical variables separately and thus cannot utilize the intrinsic useful correlation information among different clinical variables. On the other hand, in those regression methods, only a single modality of data (usually only the structural MRI) is often used, without considering the complementary information that can be provided by different modalities. In this paper, we propose a general methodology, namely Multi-Modal Multi-Task (M3T) learning, to jointly predict multiple variables from multi-modal data. Here, the variables include not only the clinical variables used for regression but also the categorical variable used for classification, with different tasks corresponding to prediction of different variables. Specifically, our method contains two key components, i.e., (1) a multi-task feature selection which selects the common subset of relevant features for multiple variables from each modality, and (2) a multi-modal support vector machine which fuses the above-selected
