What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Internal medicine
Hervé Delingette mainly investigates Artificial intelligence, Computer vision, Segmentation, Image segmentation and Simulation.
His biological study spans a wide range of topics, including Estimation theory and Magnetic resonance imaging.
His study looks at the relationship between Computer vision and fields such as Pattern recognition, as well as how they intersect with chemical problems.
His Scale-space segmentation study, which is part of a larger body of work in Segmentation, is frequently linked to Process, bridging the gap between disciplines.
His studies deal with areas such as Smoothness and Connected component as well as Image segmentation.
His Simulation study incorporates themes from Linear elasticity, Finite element method, Invariant, Applied mathematics and Nonlinear system.
His most cited work include:
- The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (1985 citations)
- Real-time elastic deformations of soft tissues for surgery simulation (658 citations)
- A hybrid elastic model for real-time cutting, deformations, and force feedback for surgery training and simulation (429 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Computer vision, Segmentation, Simulation and Pattern recognition.
The Artificial intelligence study combines topics in areas such as Algorithm and Machine learning.
His Computer vision research integrates issues from 3D ultrasound and Robustness.
The study incorporates disciplines such as Image processing and Surface in addition to Segmentation.
His Simulation research is multidisciplinary, relying on both Ablation, Computation, Finite element method and Cardiac electrophysiology.
His research integrates issues of Surgery and Nonlinear system in his study of Finite element method.
He most often published in these fields:
- Artificial intelligence (43.88%)
- Computer vision (27.46%)
- Segmentation (19.40%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (43.88%)
- Pattern recognition (12.54%)
- Deep learning (6.87%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Artificial neural network are his primary areas of study.
His study connects Computer vision and Artificial intelligence.
His Computer vision study combines topics from a wide range of disciplines, such as Cochlear implant and Cochlea.
His Pattern recognition study combines topics in areas such as Generative model, Feature, Cluster analysis and Motion analysis.
His research in Deep learning tackles topics such as Ground truth which are related to areas like Optic nerve and Optic chiasm.
His specific area of interest is Segmentation, where Hervé Delingette studies Image segmentation.
Between 2017 and 2021, his most popular works were:
- 3-D Consistent and Robust Segmentation of Cardiac Images by Deep Learning With Spatial Propagation (87 citations)
- Learning a Probabilistic Model for Diffeomorphic Registration (82 citations)
- 3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context (37 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Surgery
His main research concerns Artificial intelligence, Pattern recognition, Deep learning, Artificial neural network and Segmentation.
His Artificial intelligence research includes themes of Parameter estimation algorithm and Personalization.
His Pattern recognition research includes elements of Autoencoder, Probabilistic logic, Diffeomorphism and Hausdorff distance.
Hervé Delingette focuses mostly in the field of Deep learning, narrowing it down to matters related to Semi-supervised learning and, in some cases, Flow map, Feature, Flow, Motion and Supervised learning.
His Segmentation research focuses on Image segmentation in particular.
His Image segmentation study combines topics in areas such as Contextual image classification and Data modeling.
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