What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Magnetic resonance imaging
- Statistics
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 most cited work include:
- Advances in functional and structural MR image analysis and implementation as FSL. (9202 citations)
- IMPROVED OPTIMIZATION FOR THE ROBUST AND ACCURATE LINEAR REGISTRATION AND MOTION CORRECTION OF BRAIN IMAGES (7328 citations)
- A global optimisation method for robust affine registration of brain images (4885 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (39.82%)
- Neuroscience (18.58%)
- Pattern recognition (15.63%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (39.82%)
- Segmentation (15.63%)
- Machine learning (11.50%)
In recent papers he was focusing on the following fields of study:
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.
Between 2018 and 2021, his most popular works were:
- Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank (44 citations)
- Medium-term effects of SARS-CoV-2 infection on multiple vital organs, exercise capacity, cognition, quality of life and mental health, post-hospital discharge (26 citations)
- Alteration to hippocampal volume and shape confined to cannabis dependence: a multi-site study. (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Magnetic resonance imaging
- Statistics
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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