What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Artificial intelligence, Iterative reconstruction, Computer vision, Algorithm and Imaging phantom are his primary areas of study.
His Artificial intelligence study integrates concerns from other disciplines, such as Lymph node and Pattern recognition.
Quanzheng Li works mostly in the field of Lymph node, limiting it down to topics relating to Medical diagnosis and, in certain cases, Breast cancer and Test set, as a part of the same area of interest.
His work carried out in the field of Computer vision brings together such families of science as Feature learning, Penalty method, Prior probability and Detector.
His research in the fields of Residual neural network overlaps with other disciplines such as Ordinary differential equation, Stochastic control and Bridging.
His Imaging phantom research integrates issues from Reconstruction algorithm, Positron emission tomography, Scanner and Biomedical engineering.
His most cited work include:
- Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. (982 citations)
- Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations (175 citations)
- Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations (175 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Iterative reconstruction, Algorithm and Computer vision.
His study in Deep learning, Convolutional neural network, Image quality, Noise reduction and Medical imaging falls within the category of Artificial intelligence.
His Pattern recognition research incorporates themes from Artificial neural network, Noise and Image resolution.
The study incorporates disciplines such as Imaging phantom, Iterative method, Positron emission tomography, Attenuation and Monte Carlo method in addition to Iterative reconstruction.
In his study, Function is strongly linked to Poisson distribution, which falls under the umbrella field of Algorithm.
His work in the fields of Image processing overlaps with other areas such as Maximum a posteriori estimation.
He most often published in these fields:
- Artificial intelligence (61.57%)
- Pattern recognition (33.96%)
- Iterative reconstruction (29.10%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (61.57%)
- Pattern recognition (33.96%)
- Deep learning (18.28%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Noise.
His study in Convolutional neural network, Image segmentation, Noise reduction, Iterative reconstruction and Embedding is carried out as part of his Artificial intelligence studies.
His Iterative reconstruction research focuses on subjects like Medical imaging, which are linked to Discriminative model and Image registration.
His Pattern recognition research incorporates elements of Positron emission tomography, Image, Magnetic resonance imaging and Neuroimaging.
His study in Magnetic resonance imaging is interdisciplinary in nature, drawing from both Imaging phantom and Attenuation.
His Deep learning research is multidisciplinary, incorporating elements of Image quality, Functional brain and Cohort.
Between 2019 and 2021, his most popular works were:
- Four-Dimensional Modeling of fMRI Data via Spatio–Temporal Convolutional Neural Networks (ST-CNNs) (7 citations)
- Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning With Deep Belief Networks (7 citations)
- MR-Based Attenuation Correction for Brain PET Using 3-D Cycle-Consistent Adversarial Network (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Quanzheng Li mainly focuses on Artificial intelligence, Pattern recognition, Deep learning, Correction for attenuation and Functional magnetic resonance imaging.
The Artificial intelligence study combines topics in areas such as Disease progression, Temporal Cortices, Standardized uptake value, Gradient echo and Functional brain.
His Pattern recognition research includes themes of Cross-validation, Iterative reconstruction and Medical imaging.
His research in Iterative reconstruction intersects with topics in Imaging phantom, Attenuation, Magnetic resonance imaging and Voxel.
His work in Deep learning addresses issues such as Image quality, which are connected to fields such as Image resolution, Noise reduction and Supervised learning.
The concepts of his Functional magnetic resonance imaging study are interwoven with issues in Resting state fMRI, Neuroimaging and Data set.
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