What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Yefeng Zheng spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Image segmentation.
His is involved in several facets of Artificial intelligence study, as is seen by his studies on Deep learning, Image, Object detection, Image processing and Discriminative model.
His work in the fields of Computer vision, such as Landmark, Optical flow and Tracking, overlaps with other areas such as Nonlinear dimensionality reduction.
His studies in Pattern recognition integrate themes in fields like Artificial neural network, Supervised learning, Image registration and Metric.
His Segmentation study combines topics from a wide range of disciplines, such as Transcatheter aortic, Algorithm, Magnetic resonance imaging and Aortography.
His work on Level set method as part of his general Image segmentation study is frequently connected to Line, thereby bridging the divide between different branches of science.
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)
- Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (493 citations)
- Robust point matching for nonrigid shapes by preserving local neighborhood structures (250 citations)
What are the main themes of his work throughout his whole career to date?
Yefeng Zheng mostly deals with Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Deep learning.
Artificial intelligence is closely attributed to Machine learning in his work.
The Pattern recognition study combines topics in areas such as Image processing and Magnetic resonance imaging.
His research in Segmentation focuses on subjects like Left atrium, which are connected to Anatomy.
His Computer vision research integrates issues from Boundary and Robustness.
His Deep learning research includes themes of Artificial neural network and Training set.
He most often published in these fields:
- Artificial intelligence (79.43%)
- Pattern recognition (37.66%)
- Segmentation (36.71%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (79.43%)
- Pattern recognition (37.66%)
- Deep learning (18.67%)
In recent papers he was focusing on the following fields of study:
Yefeng Zheng mainly investigates Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Machine learning.
His study in Image, Convolutional neural network, Artificial neural network, Training set and Feature extraction is carried out as part of his Artificial intelligence studies.
His study in the fields of Discriminative model under the domain of Pattern recognition overlaps with other disciplines such as Consistency.
His Deep learning research is multidisciplinary, incorporating perspectives in Generator, Inference, Receiver operating characteristic and Scale.
When carried out as part of a general Segmentation research project, his work on Image segmentation is frequently linked to work in Field, therefore connecting diverse disciplines of study.
As part of the same scientific family, Yefeng Zheng usually focuses on Machine learning, concentrating on Benchmark and intersecting with Computer-aided diagnosis.
Between 2019 and 2021, his most popular works were:
- Rubik's Cube+: A self-supervised feature learning framework for 3D medical image analysis. (13 citations)
- Efficient and Effective Training of COVID-19 Classification Networks With Self-Supervised Dual-Track Learning to Rank (12 citations)
- Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs by Comparing Image Representations (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Yefeng Zheng mostly deals with Artificial intelligence, Deep learning, Pattern recognition, Segmentation and Machine learning.
With his scientific publications, his incorporates both Artificial intelligence and Color histogram.
His Deep learning research incorporates elements of Artificial neural network, Accurate segmentation and Training set.
Yefeng Zheng has researched Pattern recognition in several fields, including Computational complexity theory, Relationship extraction, Leverage and Robustness.
His work on Image segmentation as part of general Segmentation study is frequently connected to Market segmentation, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His study in the field of Margin is also linked to topics like Context and Small number.
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