Joint Diagnosis and Conversion Time Prediction of Progressive Mild Cognitive Impairment (pMCI) Using Low-Rank Subspace Clustering and Matrix Completion
Identifying progressive mild cognitive impairment (pMCI) patients and predicting when they will convert to Alzheimer’s disease (AD) are important for early medical intervention. Multi-modality and longitudinal data provide a great amount of information for improving diagnosis and prognosis. But thes...
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Published in | Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 Vol. 9351; pp. 527 - 534 |
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Main Authors | , , , |
Format | Book Chapter Journal Article |
Language | English |
Published |
Cham
Springer International Publishing
01.10.2015
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Series | Lecture Notes in Computer Science |
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Abstract | Identifying progressive mild cognitive impairment (pMCI) patients and predicting when they will convert to Alzheimer’s disease (AD) are important for early medical intervention. Multi-modality and longitudinal data provide a great amount of information for improving diagnosis and prognosis. But these data are often incomplete and noisy. To improve the utility of these data for prediction purposes, we propose an approach to denoise the data, impute missing values, and cluster the data into low-dimensional subspaces for pMCI prediction. We assume that the data reside in a space formed by a union of several low-dimensional subspaces and that similar MCI conditions reside in similar subspaces. Therefore, we first use incomplete low-rank representation (ILRR) and spectral clustering to cluster the data according to their representative low-rank subspaces. At the same time, we denoise the data and impute missing values. Then we utilize a low-rank matrix completion (LRMC) framework to identify pMCI patients and their time of conversion. Evaluations using the ADNI dataset indicate that our method outperforms conventional LRMC method. |
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AbstractList | Identifying progressive mild cognitive impairment (pMCI) patients and predicting when they will convert to Alzheimer’s disease (AD) are important for early medical intervention. Multi-modality and longitudinal data provide a great amount of information for improving diagnosis and prognosis. But these data are often incomplete and noisy. To improve the utility of these data for prediction purposes, we propose an approach to denoise the data, impute missing values, and cluster the data into low-dimensional subspaces for pMCI prediction. We assume that the data reside in a space formed by a union of several low-dimensional subspaces and that similar MCI conditions reside in similar subspaces. Therefore, we first use incomplete low-rank representation (ILRR) and spectral clustering to cluster the data according to their representative low-rank subspaces. At the same time, we denoise the data and impute missing values. Then we utilize a low-rank matrix completion (LRMC) framework to identify pMCI patients and their time of conversion. Evaluations using the ADNI dataset indicate that our method outperforms conventional LRMC method. |
Author | Thung, Kim-Han Shen, Dinggang Yap, Pew-Thian Adeli-M, Ehsan |
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BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27054201$$D View this record in MEDLINE/PubMed |
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References | 25955998 - IEEE J Biomed Health Inform. 2015 Sep;19(5):1610-6 24480301 - Neuroimage. 2014 May 1;91:386-400 23932184 - Alzheimers Dement. 2013 Sep;9(5):e111-94 21992749 - Neuroimage. 2012 Jan 16;59(2):895-907 12575879 - IEEE Trans Med Imaging. 2002 Nov;21(11):1421-39 16631882 - Lancet. 2006 Apr 15;367(9518):1262-70 22003753 - Med Image Comput Comput Assist Interv. 2011;14(Pt 3):635-42 22498655 - Neuroimage. 2012 Jul 2;61(3):622-32 11395428 - Bioinformatics. 2001 Jun;17(6):520-5 25926791 - Front Aging Neurosci. 2015 Apr 14;7:48 22487984 - IEEE Trans Pattern Anal Mach Intell. 2013 Jan;35(1):171-84 |
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Snippet | Identifying progressive mild cognitive impairment (pMCI) patients and predicting when they will convert to Alzheimer’s disease (AD) are important for early... Identifying progressive mild cognitive impairment (pMCI) patients and predicting when they will convert to Alzheimer's disease (AD) are important for early... |
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SubjectTerms | Conversion Time Magnetic Resonance Imaging Data Mild Cognitive Impairment Spectral Cluster Subspace Cluster |
Title | Joint Diagnosis and Conversion Time Prediction of Progressive Mild Cognitive Impairment (pMCI) Using Low-Rank Subspace Clustering and Matrix Completion |
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