Littérature scientifique sur le sujet « Brain aging, MRI, machine learning »
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Articles de revues sur le sujet "Brain aging, MRI, machine learning"
Shamir, Lior, et Joe Long. « Quantitative Machine Learning Analysis of Brain MRI Morphology throughout Aging ». Current Aging Science 9, no 4 (14 octobre 2016) : 310–17. http://dx.doi.org/10.2174/1874609809666160413113711.
Texte intégralVaranasi, Sravani, Roopan Tuli, Fei Han, Rong Chen et Fow-Sen Choa. « Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques ». Sensors 23, no 3 (1 février 2023) : 1603. http://dx.doi.org/10.3390/s23031603.
Texte intégralLee, Won Hee. « The Choice of Machine Learning Algorithms Impacts the Association between Brain-Predicted Age Difference and Cognitive Function ». Mathematics 11, no 5 (2 mars 2023) : 1229. http://dx.doi.org/10.3390/math11051229.
Texte intégralGómez-Ramírez, Jaime, Miguel A. Fernández-Blázquez et Javier J. González-Rosa. « Prediction of Chronological Age in Healthy Elderly Subjects with Machine Learning from MRI Brain Segmentation and Cortical Parcellation ». Brain Sciences 12, no 5 (29 avril 2022) : 579. http://dx.doi.org/10.3390/brainsci12050579.
Texte intégralKnight, S., R. Boyle, L. Newman, J. Davis, R. Rizzo, E. Duggan, C. De Looze, R. Whelan, R. A. Kenny et R. Romero-Ortuno. « 78 HIGHER NEUROVASCULAR SIGNAL ENTROPY IS ASSOCIATED WITH ACCELERATED BRAIN AGEING ». Age and Ageing 50, Supplement_3 (novembre 2021) : ii9—ii41. http://dx.doi.org/10.1093/ageing/afab219.78.
Texte intégralMadole, James, James W. Madole, Simon R. Cox, Colin R. Buchanan, Stuart J. Ritchie, Mark E. Bastin, Ian J. Deary et Elliot M. Tucker-Drob. « PREDICTING TRANSDIAGNOSTIC PSYCHOPATHOLOGY FROM INDICES OF AGING IN THE HUMAN STRUCTURAL CONNECTOME ». Innovation in Aging 3, Supplement_1 (novembre 2019) : S348. http://dx.doi.org/10.1093/geroni/igz038.1261.
Texte intégralGuo, Yingying, Xi Yang, Zilong Yuan, Jianfeng Qiu et Weizhao Lu. « A comparison between diffusion tensor imaging and generalized q-sampling imaging in the age prediction of healthy adults via machine learning approaches ». Journal of Neural Engineering 19, no 1 (1 février 2022) : 016013. http://dx.doi.org/10.1088/1741-2552/ac4bfe.
Texte intégralMassetti, Noemi, Mirella Russo, Raffaella Franciotti, Davide Nardini, Giorgio Maria Mandolini, Alberto Granzotto, Manuela Bomba et al. « A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer’s Disease Spectrum ». Journal of Alzheimer's Disease 85, no 4 (15 février 2022) : 1639–55. http://dx.doi.org/10.3233/jad-210573.
Texte intégralCole, James H., Jonathan Underwood, Matthan W. A. Caan, Davide De Francesco, Rosan A. van Zoest, Robert Leech, Ferdinand W. N. M. Wit et al. « Increased brain-predicted aging in treated HIV disease ». Neurology 88, no 14 (3 mars 2017) : 1349–57. http://dx.doi.org/10.1212/wnl.0000000000003790.
Texte intégralBashyam, Vishnu M., Guray Erus, Jimit Doshi, Mohamad Habes, Ilya M. Nasrallah, Monica Truelove-Hill, Dhivya Srinivasan et al. « MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide ». Brain 143, no 7 (27 juin 2020) : 2312–24. http://dx.doi.org/10.1093/brain/awaa160.
Texte intégralThèses sur le sujet "Brain aging, MRI, machine learning"
Mahbod, Amirreza. « Structural Brain MRI Segmentation Using Machine Learning Technique ». Thesis, KTH, Skolan för teknik och hälsa (STH), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189985.
Texte intégralZarogianni, Eleni. « Machine learning and brain imaging in psychosis ». Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/22814.
Texte intégralAbdulkadir, Ahmed [Verfasser], et Thomas [Akademischer Betreuer] Brox. « Brain MRI analysis and machine learning for diagnosis of neurodegeneration ». Freiburg : Universität, 2018. http://d-nb.info/117696805X/34.
Texte intégralKim, Eun Young. « Machine-learning based automated segmentation tool development for large-scale multicenter MRI data analysis ». Diss., University of Iowa, 2013. https://ir.uiowa.edu/etd/4998.
Texte intégralO'Leary, Brian. « A Vertex-Based Approach to the Statistical and Machine Learning Analyses of Brain Structure ». University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1576254162111087.
Texte intégralGinsburger, Kévin. « Modeling and simulation of the diffusion MRI signal from human brain white matter to decode its microstructure and produce an anatomic atlas at high fields (3T) ». Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS158/document.
Texte intégralDiffusion Magnetic Resonance Imaging of water in the brain has proven very useful to establish a cartography of brain connections. It is the only in vivo modality to study anatomical connectivity. A few years ago, it has been shown that diffusion MRI is also a unique tool to perform virtual biopsy of cerebral tissues. However, most of current analytical models (AxCaliber, ActiveAx, CHARMED) employed for the estimation of white matter microstructure rely upon a basic modeling of white matter, with axons represented by simple cylinders and extra-axonal diffusion assumed to be Gaussian. First, a more physically plausible analytical model of the human brain white matter accounting for the time-dependence of the diffusion process in the extra-axonal space was developed for Oscillating Gradient Spin Echo (OGSE) sequence signals. A decoding tool enabling to solve the inverse problem of estimating the parameters of the white matter microstructure from the OGSE-weighted diffusion MRI signal was designed using a robust optimization scheme for parameter estimation. Second, a Big Data approach was designed to further improve the brain microstructure decoding. All the simulation tools necessary to construct computational models of brain tissues were developed in the frame of this thesis. An algorithm creating realistic white matter tissue numerical phantoms based on a spherical meshing of cell shapes was designed, enabling to generate a massive amount of virtual voxels in a computationally efficient way thanks to a GPU-based implementation. An ultra-fast simulation tool of the water molecules diffusion process in those virtual voxels was designed, enabling to generate synthetic diffusion MRI signal for each virtual voxel. A dictionary of virtual voxels containing a huge set of geometrical configurations present in white matter was built. This dictionary contained virtual voxels with varying degrees of axonal beading, a swelling of the axonal membrane which occurs after strokes and other pathologies. The set of synthetic signals and associated geometrical configurations of the corresponding voxels was used as a training data set for a machine learning algorithm designed to decode white matter microstructure from the diffusion MRI signal and estimate the degree of axonal beading. This decoder showed encouraging regression results on unknown simulated data, showing the potential of the presented approach to characterize the microstructure of healthy and injured brain tissues in vivo. The microstructure decoding tools developed during this thesis will in particular be used to characterize white matter tissue microstructural parameters (axonal density, mean axonal diameter, glial density, mean glial cells diameter, microvascular density ) in short and long bundles. The simulation tools developed in the frame of this thesis will enable the construction of a probabilistic atlas of the white matter bundles microstructural parameters, using a mean propagator based diffeomorphic registration tool also designed in the frame of this thesis to register each individual
SALVATORE, CHRISTIAN. « Development and validation of a Decision Support System for the automatic diagnosis of medical images from brain MRI studies ». Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/94834.
Texte intégralMagrì, Salvatore. « Characterization of cerebral cortex folding in humans through MRI : quality control and dementia prediction ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21245/.
Texte intégralOlešová, Kristína. « Klasifikace stupně gliomů v MR datech mozku ». Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413113.
Texte intégralYAMIN, MUHAMMAD ABUBAKAR. « Investigating Brain Functional Networks in a Riemannian Framework ». Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1040663.
Texte intégralChapitres de livres sur le sujet "Brain aging, MRI, machine learning"
Kodner, Boris, Shiri Gordon, Jacob Goldberger et Tammy Riklin Raviv. « Atlas of Classifiers for Brain MRI Segmentation ». Dans Machine Learning in Medical Imaging, 36–44. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67389-9_5.
Texte intégralYu, Renping, Minghui Deng, Pew-Thian Yap, Zhihui Wei, Li Wang et Dinggang Shen. « Learning-Based 3T Brain MRI Segmentation with Guidance from 7T MRI Labeling ». Dans Machine Learning in Medical Imaging, 213–20. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47157-0_26.
Texte intégralChen, Xu, Chunfeng Lian, Li Wang, Pew-Thian Yap, James J. Xia et Dinggang Shen. « Segmenting Bones from Brain MRI via Generative Adversarial Learning ». Dans Machine Learning in Dentistry, 27–40. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71881-7_3.
Texte intégralDong, Pei, Xiaohuan Cao, Jun Zhang, Minjeong Kim, Guorong Wu et Dinggang Shen. « Efficient Groupwise Registration for Brain MRI by Fast Initialization ». Dans Machine Learning in Medical Imaging, 150–58. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67389-9_18.
Texte intégralValverde, Juan Miguel, Artem Shatillo, Riccardo De Feo, Olli Gröhn, Alejandra Sierra et Jussi Tohka. « Automatic Rodent Brain MRI Lesion Segmentation with Fully Convolutional Networks ». Dans Machine Learning in Medical Imaging, 195–202. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32692-0_23.
Texte intégralBao, Dongxing, Xiaoming Li et Jin Li. « Lorentzian Norm Based Super-Resolution Reconstruction of Brain MRI Image ». Dans Machine Learning and Intelligent Communications, 326–32. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73447-7_36.
Texte intégralTahir, Muhammad. « Brain MRI Classification Using Gradient Boosting ». Dans Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology, 294–301. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66843-3_29.
Texte intégralRoy, Snehashis, Aaron Carass, Jerry L. Prince et Dzung L. Pham. « Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation ». Dans Machine Learning in Medical Imaging, 248–55. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10581-9_31.
Texte intégralHabib, Md Rawshan, Ahmed Yousuf Suhan, Abhishek Vadher, Md Ashiqur Rahman Swapno, Md Rashedul Arefin, Saiful Islam, Khan Anik Rahman et Md Shahnewaz Tanvir. « Clustering of MRI in Brain Images Using Fuzzy C Means Algorithm ». Dans Machine Learning and Autonomous Systems, 437–48. Singapore : Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7996-4_31.
Texte intégralGuan, Hao, Erkun Yang, Li Wang, Pew-Thian Yap, Mingxia Liu et Dinggang Shen. « Linking Adolescent Brain MRI to Obesity via Deep Multi-cue Regression Network ». Dans Machine Learning in Medical Imaging, 111–19. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59861-7_12.
Texte intégralActes de conférences sur le sujet "Brain aging, MRI, machine learning"
Chaphekarande, Prachi, et Deepa Deshpande. « Machine Learning Based Brain MRI Estimation Method ». Dans 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE, 2019. http://dx.doi.org/10.1109/icicict46008.2019.8993363.
Texte intégralDasanayaka, Sasmitha, Sanju Silva, Vimuth Shantha, Dulani Meedeniya et Thanuja Ambegoda. « Interpretable Machine Learning for Brain Tumor Analysis Using MRI ». Dans 2022 2nd International Conference on Advanced Research in Computing (ICARC). IEEE, 2022. http://dx.doi.org/10.1109/icarc54489.2022.9754131.
Texte intégralQu, Yili, Chufu Deng, Wanqi Su, Ying Wang, Yutong Lu et Zhiguang Chen. « Multimodal Brain MRI Translation Focused on Lesions ». Dans ICMLC 2020 : 2020 12th International Conference on Machine Learning and Computing. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3383972.3384024.
Texte intégralYong Yang, Ni-Ni Rao et Shu-Ying Huang. « A novel fuzzy approach for segmentation of brain MRI ». Dans 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620871.
Texte intégralB, Preetika, M. Latha, M. Senthilmurugan et R. Chinnaiyan. « MRI Image based Brain Tumour Segmentation using Machine Learning Classifiers ». Dans 2021 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2021. http://dx.doi.org/10.1109/iccci50826.2021.9402508.
Texte intégralAfshar, Leila Keshavarz, et Hedieh Sajedi. « Age Prediction based on Brain MRI Images using Extreme Learning Machine ». Dans 2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS). IEEE, 2019. http://dx.doi.org/10.1109/cfis.2019.8692156.
Texte intégralParveen, Afiya, et Prabha Selvaraj. « Machine Learning Techniques for analysis of AD Detection using brain MRI ». Dans 2022 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2022. http://dx.doi.org/10.1109/iccci54379.2022.9740739.
Texte intégralJayasuriya, Surani Anuradha, et Alan Wee-Chung Liew. « Fractal dimension as a symmetry measure in 3D brain MRI analysis ». Dans 2012 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2012. http://dx.doi.org/10.1109/icmlc.2012.6359511.
Texte intégralDonnelly Kehoe, P., G. Pascariello, M. Quaglino, J. Nagel et J. C. Gómez. « The changing brain in healthy aging : a multi-MRI machine and multicenter surface-based morphometry study ». Dans 12th International Symposium on Medical Information Processing and Analysis, sous la direction de Eduardo Romero, Natasha Lepore, Jorge Brieva et Ignacio Larrabide. SPIE, 2017. http://dx.doi.org/10.1117/12.2256894.
Texte intégralAsodekar, Bhagyashri H., Sonal A. Gore et A. D. Thakare. « Brain Tumor analysis Based on Shape Features of MRI using Machine Learning ». Dans 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA). IEEE, 2019. http://dx.doi.org/10.1109/iccubea47591.2019.9129512.
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