What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Internal medicine
Bogdan Georgescu spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Image.
His is involved in several facets of Artificial intelligence study, as is seen by his studies on Discriminative model, Object, Deep learning, Artificial neural network and Object detection.
His work investigates the relationship between Computer vision and topics such as Robustness that intersect with problems in Convolutional neural network and Nearest neighbor search.
His Pattern recognition research incorporates elements of Boosting and Feature.
The study incorporates disciplines such as Cluster analysis and Database in addition to Segmentation.
His study looks at the intersection of Image segmentation and topics like Feature extraction with Feature selection.
His most cited work include:
- Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features (551 citations)
- Synergism in low level vision (361 citations)
- Edge detection with embedded confidence (353 citations)
What are the main themes of his work throughout his whole career to date?
Bogdan Georgescu mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Internal medicine and Cardiology.
His research brings together the fields of Machine learning and Artificial intelligence.
His study connects Robustness and Computer vision.
The concepts of his Pattern recognition study are interwoven with issues in Object detection, Boosting, Deep learning and Curse of dimensionality.
His Cardiac electrophysiology, Endocardium and Ventricle study in the realm of Internal medicine interacts with subjects such as Volume and Patient specific.
His research on Cardiology often connects related topics like Radiology.
He most often published in these fields:
- Artificial intelligence (62.26%)
- Computer vision (42.02%)
- Pattern recognition (27.24%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (62.26%)
- Pattern recognition (27.24%)
- Image (12.84%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Image, Computer vision and Segmentation.
Within one scientific family, Bogdan Georgescu focuses on topics pertaining to Machine learning under Artificial intelligence, and may sometimes address concerns connected to Rendering.
His studies deal with areas such as Modality, Curse of dimensionality and Image translation as well as Pattern recognition.
His work deals with themes such as Anatomical landmark and Translation, which intersect with Image.
His study in the fields of Landmark, Image registration and Ground truth under the domain of Computer vision overlaps with other disciplines such as Process.
His Segmentation research incorporates themes from Discriminative model and Computed tomography.
Between 2015 and 2021, his most popular works were:
- A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. (153 citations)
- Automatic Liver Segmentation Using Adversarial Image-to-Image Network (114 citations)
- Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans (105 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Computer vision, Image, Deep learning and Pattern recognition.
His research is interdisciplinary, bridging the disciplines of Machine learning and Artificial intelligence.
His Computer vision study combines topics from a wide range of disciplines, such as Sequence and State.
In general Image study, his work on Liver segmentation often relates to the realm of Volume, Spatial analysis and Subject matter, thereby connecting several areas of interest.
His Deep learning research incorporates elements of Image processing, Feature detection, Object, Feature extraction and Reinforcement learning.
His study in the field of Classification result and Feature vector also crosses realms of Decision networks and Endoscopic image.
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