What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Internal medicine
- Magnetic resonance imaging
His primary areas of study are Artificial intelligence, Segmentation, Magnetic resonance imaging, Pathology and Computer vision.
His Artificial intelligence research incorporates elements of Machine learning and Pattern recognition.
Sebastien Ourselin focuses mostly in the field of Segmentation, narrowing it down to matters related to Data mining and, in some cases, Categorical variable.
His Magnetic resonance imaging research is multidisciplinary, incorporating elements of Positron emission tomography, Nuclear medicine, Neuroimaging and Brain mapping.
The various areas that Sebastien Ourselin examines in his Pathology study include Voxel-based morphometry and Neuroscience.
His Neuroscience research incorporates themes from White matter and Disease.
His most cited work include:
- Fast free-form deformation using graphics processing units (674 citations)
- Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations (525 citations)
- Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations (525 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Magnetic resonance imaging.
Many of his studies on Artificial intelligence apply to Machine learning as well.
His Computer vision research includes themes of Imaging phantom, Robustness and Medical imaging.
Sebastien Ourselin mostly deals with Scale-space segmentation in his studies of Segmentation.
Sebastien Ourselin interconnects Positron emission tomography, Nuclear medicine, Atrophy, Pathology and Neuroimaging in the investigation of issues within Magnetic resonance imaging.
His studies deal with areas such as Alzheimer's disease and Frontotemporal dementia as well as Atrophy.
He most often published in these fields:
- Artificial intelligence (38.05%)
- Computer vision (18.61%)
- Segmentation (19.14%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (38.05%)
- Pattern recognition (16.34%)
- Segmentation (19.14%)
In recent papers he was focusing on the following fields of study:
Sebastien Ourselin focuses on Artificial intelligence, Pattern recognition, Segmentation, Convolutional neural network and Magnetic resonance imaging.
His research integrates issues of Machine learning and Computer vision in his study of Artificial intelligence.
Sebastien Ourselin has researched Pattern recognition in several fields, including Domain, Modality, Neuroimaging and Medical imaging.
His Magnetic resonance imaging research is under the purview of Radiology.
Between 2018 and 2021, his most popular works were:
- Real-time tracking of self-reported symptoms to predict potential COVID-19. (455 citations)
- Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study. (394 citations)
- Rapid implementation of mobile technology for real-time epidemiology of COVID-19. (137 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Statistics
His primary areas of study are Artificial intelligence, Segmentation, Pattern recognition, Disease and Internal medicine.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Computer vision.
His Segmentation research focuses on Overfitting and how it relates to Algorithm and Set.
Sebastien Ourselin does research in Pattern recognition, focusing on Image segmentation specifically.
His Frontotemporal dementia study in the realm of Disease interacts with subjects such as Anosmia.
His study in Convolutional neural network is interdisciplinary in nature, drawing from both Magnetic resonance imaging and Dice.
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