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MCI Identification by Joint Learning on Multiple MRI Data [chapter]

Yue Gao, Chong-Yaw Wee, Minjeong Kim, Panteleimon Giannakopoulos, Marie-Louise Montandon, Sven Haller, Dinggang Shen
2015 Lecture Notes in Computer Science  
We then combine all centralized hypergraphs by learning the optimal weight of each hypergraph based on the minimum Laplacian.  ...  Recent studies [4, 11] show great promises for integrating multiple modalities, e.g., MRI, PET and CSF, for improving AD/MCI diagnosis accuracy, and semi-supervised learning for  ...  Conclusion In this paper, we proposed a centralized hypergraph learning method to model the relationship among subjects with multiple MRIs for MCI identification.  ... 
doi:10.1007/978-3-319-24571-3_10 pmid:26942232 pmcid:PMC4773025 fatcat:7ugt6wusjfayzcyf4ynrtd2t7i

Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-status Dementia Diagnosis [chapter]

Tao Zhou, Kim-Han Thung, Xiaofeng Zhu, Dinggang Shen
2017 Lecture Notes in Computer Science  
In the third stage, we learn the diagnostic labels by fusing the learned joint latent features from the second stage.  ...  In the second stage, we learn the joint latent features for each pair of modality combination by using the high-level features learned from the first stage.  ...  HHS Public Access Author manuscript Acknowledgments This research was supported in part by NIH grants EB006733, EB008374, EB009634, MH100217, AG041721 and AG042599.  ... 
doi:10.1007/978-3-319-67389-9_16 pmid:29376149 pmcid:PMC5786435 fatcat:4rienl77gnfa5fd6coyymfblxy

A Novel Multi-relation Regularization Method for Regression and Classification in AD Diagnosis [chapter]

Xiaofeng Zhu, Heung-Il Suk, Dinggang Shen
2014 Lecture Notes in Computer Science  
By imposing these three relational characteristics along with the L2,1-norm on the weight coefficients, we formulate a new objective function.  ...  We conducted clinical score prediction and disease status identification jointly on the Alzheimer's Disease Neuroimaging Initiative dataset.  ...  Xiaofeng Zhu was partly supported by the National Natural Science Foundation of China under grant 61263035.  ... 
doi:10.1007/978-3-319-10443-0_51 pmid:25320825 pmcid:PMC6892168 fatcat:go3sixpg7ngpnlalwizn4eqame

Manifold regularized multitask feature learning for multimodality disease classification

Biao Jie, Daoqiang Zhang, Bo Cheng, Dinggang Shen
2014 Human Brain Mapping  
Specifically, we denote the feature learning on each modality as a single task, and use group-sparsity regularizer to capture the intrinsic relatedness among multiple tasks (i.e., modalities) and jointly  ...  Accordingly, in this paper we propose a manifold regularized multitask feature learning method to preserve both the intrinsic relatedness among multiple modalities of data and the data distribution information  ...  Therefore, many studies focus on possible identification of such changes at the early stage, that is, mild cognitive impairment (MCI), by leveraging neuroimaging data [Jie et al., 2014a; Sui et al., 2012  ... 
doi:10.1002/hbm.22642 pmid:25277605 pmcid:PMC4470367 fatcat:qhl4k5dmljeite3nuywmooftdy

Manifold Regularized Multi-Task Feature Selection for Multi-Modality Classification in Alzheimer's Disease [chapter]

Biao Jie, Daoqiang Zhang, Bo Cheng, Dinggang Shen
2013 Lecture Notes in Computer Science  
Although there are a number of existing multi-modality methods, few of them have addressed the problem of joint identification of disease-related brain regions from multi-modality data for classification  ...  To validate our method, we have performed extensive evaluations on the baseline Magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data of Alzheimer's Disease  ...  This work was supported in part by NIH grants EB006733, EB008374, EB009634, and AG041721, SRFDP grant (No. 20123218110009), NUAAFRF grant (No. NE2013105), and also UNSFA grant (No. KJ2013Z095).  ... 
doi:10.1007/978-3-642-40811-3_35 fatcat:435f3z7pdnd7fgfhjpu756dcie

Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer's Disease and mild cognitive impairment identification

Feng Liu, Chong-Yaw Wee, Huafu Chen, Dinggang Shen
2014 NeuroImage  
Impairment (MCI) identification.  ...  Recent emergence of multi-task learning approach makes the joint feature selection from different modalities possible.  ...  ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles.  ... 
doi:10.1016/j.neuroimage.2013.09.015 pmid:24045077 pmcid:PMC3849328 fatcat:svjzwkhacncxtgzjcgza3zjkde

Survey Based Study: Classification of Patients with Alzheimer's Disease

Shaimaa A. Al-Majeed, Mohammed S. H. Al- Tamimi
2020 Iraqi Journal of Science  
Recently, numerous researches have been undertaken on the identification of AD based on neuroimaging data, including images with radiographs and algorithms for master learning.  ...  For the clinical diagnosis of patients with Alzheimer's Disease (AD) or Mild Cognitive Impairs (MCI), the accurate identification of patients from normal control persons (NCs) is critical.  ...  These results were better than those achieved from other state-of-theart approaches, such as Multiple Kernel Learning (MKL) and Joint Regression.  ... 
doi:10.24996/ijs.2020.61.11.31 fatcat:6aveka5crnb5vnijqia4d24dl4

Matrix-Similarity Based Loss Function and Feature Selection for Alzheimer's Disease Diagnosis

Xiaofeng Zhu, Heung-Il Suk, Dinggang Shen
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition  
The newly devised loss function is combined with a group lasso method for joint feature selection across tasks, i.e., clinical scores prediction and disease status identification.  ...  In this paper, we consider the problems of joint regression and classification for AD/MCI diagnosis and propose a novel matrix-similarity based loss function that uses high-level information inherent in  ...  Xiaofeng Zhu was partly supported by the Natural Science Foundation of China (NSFC) under grant 61263035.  ... 
doi:10.1109/cvpr.2014.395 pmid:26379415 pmcid:PMC4569014 dblp:conf/cvpr/ZhuSS14 fatcat:oo752rev3fg43atx4jx24cqqm4

Canonical feature selection for joint regression and multi-class identification in Alzheimer's disease diagnosis

Xiaofeng Zhu, Heung-Il Suk, Seong-Whan Lee, Dinggang Shen
2015 Brain Imaging and Behavior  
Fusing information from different imaging modalities is crucial for more accurate identification of the brain state because imaging data of different modalities can provide complementary perspectives on  ...  We then perform the sparse multi-task learning for discriminative feature selection by using the canonical features as regressors and penalizing a loss function with a canonical regularizer.  ...  Acknowledgments This work was supported in part by NIH grants (EB006733, EB008374  ... 
doi:10.1007/s11682-015-9430-4 pmid:26254746 pmcid:PMC4747862 fatcat:ce5aejjgpfhahc5gmf7ouvoxne

Joint identification of imaging and proteomics biomarkers of Alzheimer's disease using network-guided sparse learning

Jingwen Yan, Heng Huang, Sungeun Kim, Jason Moore, Andrew Saykin, Li Shen
2014 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)  
However, the relationship among multiple data modalities are often ignored or oversimplified in prior studies.  ...  We apply this model to predict cognitive outcome from imaging and proteomic data, and show that the proposed model not only outperforms traditional ones, but also yields stable multimodal biomarkers across  ...  Acknowledgments This research was supported by NIH R01 LM011360, U01 AG024904, RC2 AG036535, R01 AG19771, P30 AG10133, and NSF IIS-1117335 at IU, by NSF CCF-0830780, CCF-0917274, DMS-0915228, and IIS  ... 
doi:10.1109/isbi.2014.6867958 pmid:25408822 pmcid:PMC4232946 fatcat:z532s3lmrzgwfanvfldd63gu7e

A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer's Disease Diagnosis

Le An, Ehsan Adeli, Mingxia Liu, Jun Zhang, Seong-Whan Lee, Dinggang Shen
2017 Scientific Reports  
For validation, we conduct experiments on AD diagnosis by selecting mutually informative features from both MRI and SNP, and using the most discriminative samples for training.  ...  Classification is one of the most important tasks in machine learning. Due to feature redundancy or outliers in samples, using all available data for training a classifier may be suboptimal.  ...  Acknowledgments This work was supported in part by NIH grants (EB006733, EB008374, MH100217, MH108914, AG041721, AG049371, AG042599, AG053867, EB022880, MH110274).  ... 
doi:10.1038/srep45269 pmid:28358032 pmcid:PMC5372170 fatcat:kp2npfvym5b4zpxzaia3ogj4p4

Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheimer's Disease Diagnosis

Mingxia Liu, Jun Zhang, Ehsan Adeli, Dinggang Shen
2018 IEEE Transactions on Biomedical Engineering  
We evaluate the proposed method on four large multi-center cohorts with 1, 984 subjects, and the experimental results demonstrate that DM 2 L is superior to several state-of-the-art joint learning methods  ...  Specifically, we first identify the discriminative anatomical landmarks from MR images in a data-driven manner, and then extract multiple image patches around these detected landmarks.  ...  Based on these image patches, we then learn representations of MRI for joint classification and regression.  ... 
doi:10.1109/tbme.2018.2869989 pmid:30222548 fatcat:sysfqhaywfeoxeomt2uge6nymi

A list of publications describing new supervised learning pipelines to predict clinical variables from neuroimaging data in Alzheimer's disease

Alex F Mendelson
2016 Figshare  
MCI Identification by Joint Learning on Multiple MRI Data. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015, pages 78-85. Springer, 2015. [163] S. Iram, P. Fergus, D.  ...  Biomedical Engineering, IEEE Transactions on, 61(2):576-589, 2014. [172] B. Jie, D. Zhang, H.-I. Suk, C.-Y. Wee, and D. Shen. Integrating multiple network properties for MCI identification.  ... 
doi:10.6084/m9.figshare.3435752 fatcat:mdtfbkinjzfezcg6qhhwx43s4e

A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis

Xiaofeng Zhu, Heung-Il Suk, Dinggang Shen
2014 NeuroImage  
Recent studies on AD/MCI diagnosis have shown that the tasks of identifying brain disease and predicting clinical scores are highly related to each other.  ...  In this paper, we consider the problems of joint regression and classification for AD/MCI diagnosis and propose a novel matrix-similarity based loss function that uses high-level information inherent in  ...  Xiaofeng Zhu was partly supported by the Natural Science Foundation of China under grant 61263035.  ... 
doi:10.1016/j.neuroimage.2014.05.078 pmid:24911377 pmcid:PMC4138265 fatcat:nkmyk4esefebbncpk7vzwtrcmm

A Novel Dynamic Hyper-graph Inference Framework for Computer Assisted Diagnosis of Neuro-Diseases [chapter]

Yingying Zhu, Xiaofeng Zhu, Minjeong Kim, Daniel Kaufer, Guorong Wu
2017 Lecture Notes in Computer Science  
Since the data representation optimized in the feature domain is not exactly aligned with the clinical labels, such independent step-by-step workflow might result in sub-optimal classification.  ...  Therefore, the learned subjectwise relation-ships are neither consistent across modalities nor fully consensus with the clinical labels or clinical scores. (2) The learning procedure of data representation  ...  Conclusions In this work, we proposed a dynamic hyper-graph method for joint learning, classification, and regression on multiple classification problems (MCI/NC/AD) using multiple modal imaging data.  ... 
doi:10.1007/978-3-319-59050-9_13 pmid:30245556 pmcid:PMC6150469 fatcat:nn6nzm3v2jeq5dvr2wkkptksmi
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