What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His primary areas of study are Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Lung cancer.
His work on Image segmentation, Image processing and Robustness as part of general Artificial intelligence research is often related to Linear combination, thus linking different fields of science.
His study focuses on the intersection of Computer vision and fields such as Medical imaging with connections in the field of Image scaling, Optimization problem, State, Biometrics and Statistical model.
His Segmentation research focuses on subjects like Voxel, which are linked to Active appearance model.
His Pattern recognition research is multidisciplinary, incorporating elements of Probability distribution, Image, Grayscale, Retina and Pairwise comparison.
His study in Lung cancer is interdisciplinary in nature, drawing from both Cancer, Lung, Cad system and Mass screening.
His most cited work include:
- Precise segmentation of multimodal images (159 citations)
- Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies (139 citations)
- Models and methods for analyzing DCE-MRI: A review (137 citations)
What are the main themes of his work throughout his whole career to date?
Georgy Gimel'farb mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Image segmentation.
His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Random field.
Georgy Gimel'farb regularly links together related areas like Affine transformation in his Computer vision studies.
His work in the fields of Pattern recognition, such as Feature extraction, intersects with other areas such as Linear combination.
His Segmentation research includes elements of Lung cancer, Shape analysis and Grayscale.
His Image segmentation study incorporates themes from Parametric statistics and Robustness.
He most often published in these fields:
- Artificial intelligence (74.93%)
- Computer vision (47.76%)
- Pattern recognition (32.84%)
What were the highlights of his more recent work (between 2015-2020)?
- Artificial intelligence (74.93%)
- Pattern recognition (32.84%)
- Computer vision (47.76%)
In recent papers he was focusing on the following fields of study:
Georgy Gimel'farb spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Segmentation and Image segmentation.
Georgy Gimel'farb frequently studies issues relating to Machine learning and Artificial intelligence.
His studies in Pattern recognition integrate themes in fields like Prior probability, Magnetic resonance imaging, Invariant and Random field.
The Segmentation study combines topics in areas such as Similarity and Hausdorff distance.
His Image segmentation study integrates concerns from other disciplines, such as Creatinine, Renal transplant, Radiology and Renal function.
His research integrates issues of Discriminative model and Visual appearance in his study of Voxel.
Between 2015 and 2020, his most popular works were:
- Alzheimer's disease diagnostics by a deeply supervised adaptable 3D convolutional network (78 citations)
- Accurate Lungs Segmentation on CT Chest Images by Adaptive Appearance-Guided Shape Modeling (60 citations)
- Studying Autism Spectrum Disorder with Structural and Diffusion Magnetic Resonance Imaging: A Survey. (47 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Magnetic resonance imaging, Computer vision and Feature extraction.
Many of his studies on Artificial intelligence apply to Machine learning as well.
In his works, Georgy Gimel'farb conducts interdisciplinary research on Pattern recognition and Diagnostic system.
His Magnetic resonance imaging research incorporates themes from Classifier and Renal biopsy.
His Computer vision study combines topics from a wide range of disciplines, such as Translation, Iterative refinement and Interpolation.
His Image segmentation study in the realm of Segmentation interacts with subjects such as Marginal distribution.
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