What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Internal medicine
- Computer vision
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Segmentation, Image processing and Mass cytometry.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Pattern recognition.
He has included themes like Magnetic resonance imaging and Atlas in his Computer vision study.
His work on Image segmentation as part of general Segmentation study is frequently connected to Iterative closest point, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His work carried out in the field of Image processing brings together such families of science as Orientation, Ground truth, Pose and Silhouette.
Disease, Intestinal Disorder and Gastrointestinal tract is closely connected to Immune system in his research, which is encompassed under the umbrella topic of Mass cytometry.
His most cited work include:
- Conserved cell types with divergent features in human versus mouse cortex (406 citations)
- Conserved cell types with divergent features in human versus mouse cortex (406 citations)
- 3-D active appearance models: segmentation of cardiac MR and ultrasound images (374 citations)
What are the main themes of his work throughout his whole career to date?
Boudewijn P. F. Lelieveldt mainly focuses on Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Magnetic resonance imaging.
His work is connected to Active appearance model, Image registration, Image processing, Image segmentation and Principal component analysis, as a part of Artificial intelligence.
His Image registration research is multidisciplinary, relying on both Transformation and Nuclear medicine.
His Computer vision study combines topics in areas such as Gradient descent, Atlas, Robustness and Cardiac cycle.
His work on Active shape model as part of general Segmentation study is frequently linked to Initialization, bridging the gap between disciplines.
His study looks at the relationship between Radiology and topics such as Coronary arteries, which overlap with Coronary artery disease.
He most often published in these fields:
- Artificial intelligence (55.38%)
- Computer vision (37.37%)
- Segmentation (23.92%)
What were the highlights of his more recent work (between 2018-2021)?
- Mass cytometry (12.37%)
- Artificial intelligence (55.38%)
- Immune system (7.53%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Mass cytometry, Artificial intelligence, Immune system, Neuroscience and Image registration.
His Mass cytometry research includes elements of Cell, CD8, Data mining and Cell biology.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Pattern recognition.
His studies in Neuroscience integrate themes in fields like Transcriptome, Cell type and Single-cell analysis.
His Single-cell analysis study integrates concerns from other disciplines, such as Cerebral cortex, Middle temporal gyrus, Cortex and Excitatory postsynaptic potential.
Boudewijn P. F. Lelieveldt combines subjects such as Decoding methods, Prostate, Nuclear medicine and Regression with his study of Image registration.
Between 2018 and 2021, his most popular works were:
- Conserved cell types with divergent features in human versus mouse cortex (406 citations)
- Conserved cell types with divergent features in human versus mouse cortex (406 citations)
- Eleven grand challenges in single-cell data science (187 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Cancer
Boudewijn P. F. Lelieveldt focuses on Mass cytometry, Immune system, Neuroscience, Cell type and Human brain.
Boudewijn P. F. Lelieveldt interconnects Data mining, T cell, Antigen-presenting cell, Cell division and Cell biology in the investigation of issues within Mass cytometry.
His Neuroscience study incorporates themes from Transcriptome and Single-cell analysis.
His biological study spans a wide range of topics, including Cerebral cortex, Middle temporal gyrus, Cortex and Excitatory postsynaptic potential.
The study incorporates disciplines such as Neocortex, Epigenomics, Probabilistic logic, Gene and Epigenome in addition to Cell type.
His work deals with themes such as BAP1 and Disease, Parkinson's disease, which intersect with Human brain.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
