What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Internal medicine
His primary areas of study are Magnetic resonance imaging, Artificial intelligence, Neuroimaging, Neuroscience and Pattern recognition.
His Magnetic resonance imaging study incorporates themes from Mixed model and Pathology.
His research investigates the connection between Artificial intelligence and topics such as Computer vision that intersect with problems in Interpolation.
His Neuroimaging research is multidisciplinary, incorporating elements of Familial risk, Brain development, Linear discriminant analysis, Atlas and Pediatrics.
His research integrates issues of Voxel and Entorhinal cortex in his study of Pattern recognition.
His work investigates the relationship between Segmentation and topics such as Image processing that intersect with problems in Image segmentation.
His most cited work include:
- Unbiased Average Age-Appropriate Atlases for Pediatric Studies (1073 citations)
- Unbiased nonlinear average age-appropriate brain templates from birth to adulthood (717 citations)
- Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. (554 citations)
What are the main themes of his work throughout his whole career to date?
Vladimir S. Fonov focuses on Artificial intelligence, Pattern recognition, Magnetic resonance imaging, Segmentation and Neuroscience.
In most of his Artificial intelligence studies, his work intersects topics such as Computer vision.
His Pattern recognition research integrates issues from Brain morphometry, Outlier, Robustness and Atlas.
His Magnetic resonance imaging research is multidisciplinary, relying on both Neuroimaging, Nuclear medicine and Pathology.
The various areas that Vladimir S. Fonov examines in his Segmentation study include Image processing and Convolutional neural network.
In his study, Disease, Parkinson's disease, Alzheimer's disease and Multiple sclerosis is inextricably linked to Atrophy, which falls within the broad field of Neuroscience.
He most often published in these fields:
- Artificial intelligence (27.23%)
- Pattern recognition (20.19%)
- Magnetic resonance imaging (16.90%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (27.23%)
- Pattern recognition (20.19%)
- Cognition (8.45%)
In recent papers he was focusing on the following fields of study:
Vladimir S. Fonov spends much of his time researching Artificial intelligence, Pattern recognition, Cognition, Magnetic resonance imaging and Neuroimaging.
His Artificial intelligence study integrates concerns from other disciplines, such as Atlas and Computer vision.
In the subject of general Pattern recognition, his work in Segmentation is often linked to Process, thereby combining diverse domains of study.
His studies in Cognition integrate themes in fields like Internal medicine, Cohort and Audiology.
Vladimir S. Fonov has researched Magnetic resonance imaging in several fields, including Brain tumor and Nuclear medicine.
The concepts of his Neuroimaging study are interwoven with issues in Cartography, Middle temporal gyrus, Functional magnetic resonance imaging and Prefrontal cortex.
Between 2017 and 2021, his most popular works were:
- Network connectivity determines cortical thinning in early Parkinson's disease progression (99 citations)
- Benchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge (63 citations)
- PAM50: Unbiased multimodal template of the brainstem and spinal cord aligned with the ICBM152 space. (57 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Internal medicine
His main research concerns Artificial intelligence, Pattern recognition, Cognition, Magnetic resonance imaging and Segmentation.
His work deals with themes such as High spatial resolution, Diagnostic biomarker and Atlas, which intersect with Artificial intelligence.
His work carried out in the field of Pattern recognition brings together such families of science as Metadata, Data pre-processing, Brain morphometry, Noise and Relevance.
His Cognition research incorporates elements of Cerebellum, Schizophrenia, Lobe and Cohort.
His work on Multi contrast as part of general Magnetic resonance imaging study is frequently linked to In patient, bridging the gap between disciplines.
His studies in Segmentation integrate themes in fields like Deep learning, Convolutional neural network, Outlier and Robustness.
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