His primary areas of study are Artificial intelligence, Neuroscience, Neuroimaging, Image processing and Pattern recognition. His Artificial intelligence research includes themes of Machine learning, Diffusion MRI and Computer vision. His studies in Neuroscience integrate themes in fields like White matter, Voxel-based morphometry and Cognitive science.
His Neuroimaging study incorporates themes from Tractography, Biobank, Magnetic resonance imaging and Human brain. His Image processing research incorporates elements of Smoothing, Visualization, Voxel and Robustness. His Pattern recognition study integrates concerns from other disciplines, such as Spatial analysis and Brain mapping.
His primary areas of investigation include Artificial intelligence, Neuroscience, Pattern recognition, White matter and Neuroimaging. Mark Jenkinson interconnects Machine learning and Computer vision in the investigation of issues within Artificial intelligence. His Pattern recognition study frequently draws connections between related disciplines such as Image processing.
The study incorporates disciplines such as Multiple sclerosis, Hyperintensity, Internal medicine and Pathology in addition to White matter. His biological study spans a wide range of topics, including Magnetic resonance imaging and Diffusion MRI. Mark Jenkinson combines topics linked to Data mining with his work on Neuroimaging.
Mark Jenkinson spends much of his time researching Artificial intelligence, Segmentation, Machine learning, White matter and Scheme. His Artificial intelligence study frequently links to related topics such as Pattern recognition. His Segmentation research integrates issues from Contrast, Hyperintensity, Thresholding and Mr images.
He combines subjects such as Disease risk, Vascular disease and Computed tomography with his study of Machine learning. His work carried out in the field of White matter brings together such families of science as Neuroscience, Macaque and Genetic variation. His studies deal with areas such as Disease progression and Amyloid as well as Neuroscience.
Mark Jenkinson mainly focuses on Artificial intelligence, White matter, Pipeline, Dynamic Host Configuration Protocol and Human Connectome Project. His work deals with themes such as Machine learning and Pattern recognition, which intersect with Artificial intelligence. His White matter study combines topics from a wide range of disciplines, such as Cartography, Neuroscience, Macaque and Genetic variation.
Many of his studies on Human Connectome Project involve topics that are commonly interrelated, such as Functional magnetic resonance imaging. The Arcuate fasciculus study combines topics in areas such as Myelin, Superior longitudinal fasciculus and Human brain. His research in Superior longitudinal fasciculus intersects with topics in Tractography and Cortex.
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Advances in functional and structural MR image analysis and implementation as FSL.
S M Smith;M Jenkinson;M W Woolrich;M W Woolrich;C F Beckmann.
IMPROVED OPTIMIZATION FOR THE ROBUST AND ACCURATE LINEAR REGISTRATION AND MOTION CORRECTION OF BRAIN IMAGES
Mark Jenkinson;Peter R. Bannister;Peter R. Bannister;Michael Brady;Stephen M. Smith.
A global optimisation method for robust affine registration of brain images
Mark Jenkinson;Stephen M. Smith.
Medical Image Analysis (2001)
Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.
S M Smith;M Jenkinson;H Johansen-Berg;D Rueckert.
The minimal preprocessing pipelines for the Human Connectome Project.
Matthew F. Glasser;Stamatios N. Sotiropoulos;J. Anthony Wilson;Timothy S. Coalson.
A multi-modal parcellation of human cerebral cortex
Matthew F. Glasser;Timothy S. Coalson;Emma C. Robinson;Emma C. Robinson;Carl D. Hacker.
Bayesian analysis of neuroimaging data in FSL.
Mark William Woolrich;Saâd Jbabdi;Brian Patenaude;Michael A. Chappell.
Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.
Arno Klein;Jesper L. R. Andersson;Babak A. Ardekani;Babak A. Ardekani;John Ashburner.
Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis
Stephen M. Smith;Yongyue Zhang;Mark Jenkinson;Jacqueline Chen.
A Bayesian model of shape and appearance for subcortical brain segmentation
Brian Patenaude;Stephen M. Smith;David N. Kennedy;Mark Jenkinson.
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