What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer vision
Pew Thian Yap mainly focuses on Artificial intelligence, Pattern recognition, Magnetic resonance imaging, Neuroimaging and Diffusion MRI.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Receiver operating characteristic, Brain mapping and Computer vision.
His work on Image is typically connected to Gap filling as part of general Computer vision study, connecting several disciplines of science.
His work on Discriminative model as part of general Pattern recognition research is frequently linked to Multi-task learning, bridging the gap between disciplines.
His Neuroimaging research includes elements of Skull, Artificial neural network, Pathology, Set and Atlas.
In general Diffusion MRI study, his work on Tractography often relates to the realm of Specialization, thereby connecting several areas of interest.
His most cited work include:
- Image analysis by Krawtchouk moments (496 citations)
- Infant brain atlases from neonates to 1- and 2-year-olds. (300 citations)
- Identification of MCI individuals using structural and functional connectivity networks (264 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Pattern recognition, Computer vision, Diffusion MRI and Algorithm are his primary areas of study.
Artificial intelligence is closely attributed to Machine learning in his work.
Pew Thian Yap has researched Pattern recognition in several fields, including Magnetic resonance imaging and Cluster analysis.
His Image resolution, Image processing and Matching study in the realm of Computer vision connects with subjects such as Deformation.
His studies deal with areas such as White matter, Focus and Spherical harmonics as well as Diffusion MRI.
His Algorithm research includes themes of Orientation, Noise reduction, Invariant and Inverse problem.
He most often published in these fields:
- Artificial intelligence (70.43%)
- Pattern recognition (42.19%)
- Computer vision (24.58%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (70.43%)
- Pattern recognition (42.19%)
- Deep learning (10.63%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Pattern recognition, Deep learning, Diffusion MRI and Computer vision.
Pew Thian Yap incorporates Artificial intelligence and Field in his studies.
Many of his research projects under Pattern recognition are closely connected to Domain with Domain, tying the diverse disciplines of science together.
His biological study spans a wide range of topics, including Feature extraction and Scale.
The various areas that Pew Thian Yap examines in his Diffusion MRI study include Orientation, White matter, Volume and Human Connectome Project.
His Computer vision research incorporates themes from Facial bone, Surgical planning and Brain mri.
Between 2019 and 2021, his most popular works were:
- Identifying Autism Spectrum Disorder With Multi-Site fMRI via Low-Rank Domain Adaptation (24 citations)
- Large‐scale dynamic causal modeling of major depressive disorder based on resting‐state functional magnetic resonance imaging (10 citations)
- Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains. (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Artificial intelligence, Pattern recognition, Deep learning, Diffusion MRI and Connectome.
His Artificial intelligence study incorporates themes from Machine learning, Magnetic resonance imaging and Identification.
He interconnects Functional magnetic resonance imaging and Neuroimaging in the investigation of issues within Pattern recognition.
His Deep learning research focuses on Scale and how it connects with Feature extraction, Computer vision, Segmentation, Image registration and Image.
His work on Fractional anisotropy as part of his general Diffusion MRI study is frequently connected to Thermal diffusivity, thereby bridging the divide between different branches of science.
His work carried out in the field of Connectome brings together such families of science as Resting state fMRI, Convolutional neural network and Graph.
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