What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer vision
Jean-Philippe Thiran mostly deals with Artificial intelligence, Diffusion MRI, Computer vision, Neuroscience and Tractography.
His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition.
His work deals with themes such as White matter, Brain mapping and Connectomics, which intersect with Diffusion MRI.
His work carried out in the field of Computer vision brings together such families of science as Similarity measure and Feature vector.
His study looks at the relationship between Tractography and fields such as Nuclear magnetic resonance, as well as how they intersect with chemical problems.
His research in Magnetic resonance imaging intersects with topics in Image quality, Iterative reconstruction, Nuclear medicine and Compressed sensing.
His most cited work include:
- Predicting human resting-state functional connectivity from structural connectivity (2146 citations)
- Fast Global Minimization of the Active Contour/Snake Model (750 citations)
- Mapping human whole-brain structural networks with diffusion MRI. (621 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Diffusion MRI and Segmentation.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Magnetic resonance imaging.
His studies in Pattern recognition integrate themes in fields like Speech recognition, Face detection and Cluster analysis.
His Computer vision study integrates concerns from other disciplines, such as Robustness and Medical imaging.
Jean-Philippe Thiran has included themes like White matter, Voxel, Neuroscience and Algorithm in his Diffusion MRI study.
Particularly relevant to Connectome is his body of work in Neuroscience.
He most often published in these fields:
- Artificial intelligence (54.30%)
- Pattern recognition (29.52%)
- Computer vision (26.36%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (54.30%)
- Pattern recognition (29.52%)
- Diffusion MRI (21.04%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Pattern recognition, Diffusion MRI, Algorithm and Magnetic resonance imaging.
His Artificial intelligence research includes elements of Machine learning and Computer vision.
The study incorporates disciplines such as White matter, Similarity and Face in addition to Pattern recognition.
His work on Tractography as part of general Diffusion MRI study is frequently linked to Fiber orientation, bridging the gap between disciplines.
The concepts of his Algorithm study are interwoven with issues in Artificial neural network, Monte Carlo method and Signal.
His Magnetic resonance imaging research integrates issues from Neuroscience, Brain development and Echo.
Between 2018 and 2021, his most popular works were:
- Limits to anatomical accuracy of diffusion tractography using modern approaches. (80 citations)
- SROBB: Targeted Perceptual Loss for Single Image Super-Resolution (31 citations)
- SynDeMo: Synergistic Deep Feature Alignment for Joint Learning of Depth and Ego-Motion (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
His primary areas of investigation include Artificial intelligence, Magnetic resonance imaging, Pattern recognition, Diffusion MRI and Tractography.
His biological study spans a wide range of topics, including Machine learning and Computer vision.
His research integrates issues of Monte Carlo method and Neuroscience, Human brain in his study of Magnetic resonance imaging.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Domain, Translation, Facial expression and Face.
His Diffusion MRI study combines topics in areas such as Segmentation, Image segmentation, Algorithm and Voxel.
He has researched Tractography in several fields, including Brain anatomy and Similarity measure.
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