Generalization of diffusion magnetic resonance imaging–based brain age prediction model through transfer learning
暂无分享,去创建一个
Hai-Gwo Hwu | Chang-Le Chen | Yung-Chin Hsu | Wen-Yih Isaac Tseng | Yu-Hung Tung | Li-Ying Yang | Wen-Bin Luo | Chih-Min Liu | Tzung-Jeng Hwang | W. Tseng | H. Hwu | Chih-Min Liu | Y. Hsu | T. Hwang | Chang-Le Chen | Li-Ying Yang | Y. Tung | Wenjing Luo
[1] Olivier Potvin,et al. Bias-adjustment in neuroimaging-based brain age frameworks: A robust scheme , 2019, NeuroImage: Clinical.
[2] Yung-Chin Hsu,et al. Premature white matter aging in patients with right mesial temporal lobe epilepsy: A machine learning approach based on diffusion MRI data , 2019, NeuroImage: Clinical.
[3] Wiro J Niessen,et al. Gray Matter Age Prediction as a Biomarker for Risk of Dementia , 2019, Proceedings of the National Academy of Sciences.
[4] Anders M. Dale,et al. Common brain disorders are associated with heritable patterns of apparent aging of the brain , 2019, Nature Neuroscience.
[5] Philippe Very,et al. Hidden Covariate Shift: A Minimal Assumption For Domain Adaptation , 2019, ArXiv.
[6] Stephen M. Smith,et al. Estimation of brain age delta from brain imaging , 2019, NeuroImage.
[7] Wouter M. Kouw. An introduction to domain adaptation and transfer learning , 2018, ArXiv.
[8] Christopher Bowd,et al. Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs , 2018, Scientific Reports.
[9] Sridar Narayanan,et al. Scan-rescan repeatability and cross-scanner comparability of DTI metrics in healthy subjects in the SPRINT-MS multicenter trial. , 2018, Magnetic resonance imaging.
[10] Wenjian Qin,et al. Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination , 2018, BioMed research international.
[11] J. Cole,et al. Neuroimaging Studies Illustrate the Commonalities Between Ageing and Brain Diseases , 2018, BioEssays : news and reviews in molecular, cellular and developmental biology.
[12] Sune Nørhøj Jespersen,et al. White matter biomarkers from diffusion MRI , 2018 .
[13] Lars T. Westlye,et al. Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry , 2018, bioRxiv.
[14] L. Vécsei,et al. Is diffusion magnetic resonance imaging the future biomarker to measure therapeutic efficacy in multiple sclerosis? , 2018, European journal of neurology.
[15] Uwe Kruger,et al. 3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network , 2018, IEEE Transactions on Medical Imaging.
[16] J. Cole,et al. Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers , 2017, Trends in Neurosciences.
[17] Thomas Thesen,et al. Structural brain changes in medically refractory focal epilepsy resemble premature brain aging , 2017, Epilepsy Research.
[18] Daniel L. Rubin,et al. Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma , 2017, Comput. Medical Imaging Graph..
[19] Stuart J. Ritchie,et al. Brain age predicts mortality , 2017, Molecular Psychiatry.
[20] Nico Karssemeijer,et al. Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation , 2017, MICCAI.
[21] Peter Savadjiev,et al. Multi-site harmonization of diffusion MRI data in a registration framework , 2017, Brain Imaging and Behavior.
[22] Seyed Abolfazl Valizadeh,et al. Age prediction on the basis of brain anatomical measures , 2017, Human brain mapping.
[23] Cam-CAN Group,et al. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample , 2017, NeuroImage.
[24] Mark E Bastin,et al. Ageing and brain white matter structure in 3,513 UK Biobank participants , 2016, Nature Communications.
[25] Giovanni Montana,et al. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker , 2016, NeuroImage.
[26] Daniel S. Margulies,et al. Predicting brain-age from multimodal imaging data captures cognitive impairment , 2016, NeuroImage.
[27] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Peter Savadjiev,et al. Inter-site and inter-scanner diffusion MRI data harmonization , 2016, NeuroImage.
[29] Karl R. Weiss,et al. A survey of transfer learning , 2016, Journal of Big Data.
[30] Wei Huang,et al. Demonstration of nonlinearity bias in the measurement of the apparent diffusion coefficient in multicenter trials , 2016, Magnetic resonance in medicine.
[31] Wiepke Cahn,et al. Accelerated Brain Aging in Schizophrenia: A Longitudinal Pattern Recognition Study. , 2016, The American journal of psychiatry.
[32] Carlo Pierpaoli,et al. Clinical feasibility of using mean apparent propagator (MAP) MRI to characterize brain tissue microstructure , 2016, NeuroImage.
[33] Stamatios N. Sotiropoulos,et al. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging , 2016, NeuroImage.
[34] Peter Savadjiev,et al. Harmonizing Diffusion MRI Data Across Multiple Sites and Scanners , 2015, MICCAI.
[35] Yu-Chun Lo,et al. NTU‐DSI‐122: A diffusion spectrum imaging template with high anatomical matching to the ICBM‐152 space , 2015, Human brain mapping.
[36] Yu-Chun Lo,et al. Automatic whole brain tract‐based analysis using predefined tracts in a diffusion spectrum imaging template and an accurate registration strategy , 2015, Human brain mapping.
[37] Nick C Fox,et al. Diffusion imaging changes in grey matter in Alzheimer’s disease: a potential marker of early neurodegeneration , 2015, Alzheimer's Research & Therapy.
[38] Robert Leech,et al. Prediction of brain age suggests accelerated atrophy after traumatic brain injury , 2015, Annals of neurology.
[39] Magda Tsolaki,et al. Multisite longitudinal reliability of tract-based spatial statistics in diffusion tensor imaging of healthy elderly subjects , 2014, NeuroImage.
[40] William D. Marslen-Wilson,et al. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing , 2014, BMC Neurology.
[41] N. Gong,et al. Aging in deep gray matter and white matter revealed by diffusional kurtosis imaging , 2014, Neurobiology of Aging.
[42] D. Madden,et al. Disconnected aging: Cerebral white matter integrity and age-related differences in cognition , 2014, Neuroscience.
[43] Christos Davatzikos,et al. Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders. , 2014, Schizophrenia bulletin.
[44] Arthur W. Toga,et al. Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling , 2014, NeuroImage.
[45] David H. Salat,et al. Non-Gaussian water diffusion in aging white matter , 2014, Neurobiology of Aging.
[46] Carlo Pierpaoli,et al. Mean apparent propagator (MAP) MRI: A novel diffusion imaging method for mapping tissue microstructure , 2013, NeuroImage.
[47] Khader M. Hasan,et al. Prediction of individual subject's age across the human lifespan using diffusion tensor imaging: A machine learning approach , 2013, NeuroImage.
[48] James S Babb,et al. Non-Gaussian diffusion MRI assessment of brain microstructure in mild cognitive impairment and Alzheimer's disease. , 2013, Magnetic resonance imaging.
[49] Stefan Klöppel,et al. BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer’s Disease , 2013, PloS one.
[50] Manuel Serrano,et al. The Hallmarks of Aging , 2013, Cell.
[51] Guang Yang,et al. Evaluation of optimized b-value sampling schemas for diffusion kurtosis imaging with an application to stroke patient data , 2013, Comput. Medical Imaging Graph..
[52] Rajesh Kumar,et al. Brain axial and radial diffusivity changes with age and gender in healthy adults , 2013, Brain Research.
[53] Peter Kochunov,et al. Testing the Hypothesis of Accelerated Cerebral White Matter Aging in Schizophrenia and Major Depression , 2013, Biological Psychiatry.
[54] Yung-Chin Hsu,et al. A large deformation diffeomorphic metric mapping solution for diffusion spectrum imaging datasets , 2012, NeuroImage.
[55] Yoshua Bengio,et al. Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..
[56] Stefan Klöppel,et al. Multicenter stability of diffusion tensor imaging measures: A European clinical and physical phantom study , 2011, Psychiatry Research: Neuroimaging.
[57] Daniel L. Polders,et al. Signal to noise ratio and uncertainty in diffusion tensor imaging at 1.5, 3.0, and 7.0 Tesla , 2011, Journal of magnetic resonance imaging : JMRI.
[58] Xing Qiu,et al. Quantification of accuracy and precision of multi-center DTI measurements: A diffusion phantom and human brain study , 2011, NeuroImage.
[59] Karl J. Friston,et al. Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation , 2011, NeuroImage.
[60] Stefan Klöppel,et al. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters , 2010, NeuroImage.
[61] Neil D. Lawrence,et al. Dataset Shift in Machine Learning , 2009 .
[62] Li-Wei Kuo,et al. Optimization of diffusion spectrum imaging and q-ball imaging on clinical MRI system , 2008, NeuroImage.
[63] S. Schoenberg,et al. Measurement of signal‐to‐noise ratios in MR images: Influence of multichannel coils, parallel imaging, and reconstruction filters , 2007, Journal of magnetic resonance imaging : JMRI.
[64] A. Alexander,et al. Diffusion tensor imaging of the brain , 2007, Neurotherapeutics.
[65] Michael I. Miller,et al. Diffeomorphic Matching of Diffusion Tensor Images , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).
[66] Yu-Chien Wu,et al. Comparison of diffusion tensor imaging measurements at 3.0 T versus 1.5 T with and without parallel imaging. , 2006, Neuroimaging clinics of North America.
[67] C. Filley,et al. White Matter and Behavioral Neurology , 2005, Annals of the New York Academy of Sciences.
[68] P. Hagmann,et al. Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging , 2005, Magnetic resonance in medicine.
[69] M. Catani,et al. The rises and falls of disconnection syndromes. , 2005, Brain : a journal of neurology.
[70] Hua Jin,et al. Comparing microstructural and macrostructural development of the cerebral cortex in premature newborns: Diffusion tensor imaging versus cortical gyration , 2005, NeuroImage.
[71] J. Helpern,et al. Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imaging , 2005, Magnetic resonance in medicine.
[72] J. Weir. Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. , 2005, Journal of strength and conditioning research.
[73] D. Tuch. Q‐ball imaging , 2004, Magnetic resonance in medicine.
[74] V. Wedeen,et al. Reduction of eddy‐current‐induced distortion in diffusion MRI using a twice‐refocused spin echo , 2003, Magnetic resonance in medicine.
[75] N. Tzourio-Mazoyer,et al. Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.
[76] D. Le Bihan,et al. Diffusion tensor imaging: Concepts and applications , 2001, Journal of magnetic resonance imaging : JMRI.
[77] M. F. Møller. A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning , 1990 .
[78] R. Licandro,et al. Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis , 2018, Lecture Notes in Computer Science.
[79] Boekel. UvA-DARE (Digital Academic Repository) A test-retest reliability analysis of diffusion measures of white matter tracts relevant for cognitive control , 2016 .
[80] Ameer Pasha Hosseinbor,et al. Characterization of Cerebral White Matter Properties Using Quantitative Magnetic Resonance Imaging Stains , 2011, Brain Connect..
[81] D. Selkoe. Alzheimer's disease. , 2011, Cold Spring Harbor perspectives in biology.
[82] Martin Fodslette Møller,et al. A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.