What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Guorong Wu spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Image registration and Atlas.
His research links Magnetic resonance imaging with Artificial intelligence.
His Computer vision research is multidisciplinary, relying on both Algorithm and Point.
His studies deal with areas such as Salient, Energy and Voxel as well as Pattern recognition.
His Image registration study integrates concerns from other disciplines, such as Spatial normalization, Deep learning, Pairwise comparison and Consistency.
His Feature research incorporates themes from Unsupervised learning and Thin plate spline.
His most cited work include:
- Deep Learning in Medical Image Analysis (1360 citations)
- Infant brain atlases from neonates to 1- and 2-year-olds. (300 citations)
- Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning (170 citations)
What are the main themes of his work throughout his whole career to date?
Guorong Wu mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Image registration and Segmentation.
His Artificial intelligence study combines topics in areas such as Machine learning, Magnetic resonance imaging and Atlas.
His work in Machine learning addresses subjects such as Neuroimaging, which are connected to disciplines such as Disease.
His biological study spans a wide range of topics, including Deep learning, Pairwise comparison and Cluster analysis.
His work deals with themes such as Sparse approximation, Diffeomorphism and Medical imaging, which intersect with Computer vision.
His study in Image registration is interdisciplinary in nature, drawing from both Image warping, Spatial normalization, Feature and Mr images.
He most often published in these fields:
- Artificial intelligence (87.04%)
- Pattern recognition (48.61%)
- Computer vision (45.37%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (87.04%)
- Pattern recognition (48.61%)
- Connectome (7.41%)
In recent papers he was focusing on the following fields of study:
Guorong Wu focuses on Artificial intelligence, Pattern recognition, Connectome, Laplacian matrix and Brain network.
Artificial intelligence is closely attributed to Machine learning in his work.
Guorong Wu combines subjects such as Artificial neural network, Resting state fMRI and Curse of dimensionality with his study of Pattern recognition.
His Connectome research integrates issues from Vertex and Atlas.
His study on Laplacian matrix also encompasses disciplines like
- Stiefel manifold and related Geometric median and Geodesic,
- Manifold which intersects with area such as Linear discriminant analysis, Outlier, Regularization and Semi-supervised learning.
Within one scientific family, he focuses on topics pertaining to Feature learning under Representation, and may sometimes address concerns connected to Image registration, Feature, Data science and Image segmentation.
Between 2017 and 2021, his most popular works were:
- Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises (31 citations)
- Sliding window correlation analysis: Modulating window shape for dynamic brain connectivity in resting state. (16 citations)
- Dynamic fMRI networks predict success in a behavioral weight loss program among older adults. (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Artificial intelligence, Pattern recognition, Deep learning, Disease and Machine learning are his primary areas of study.
In the subject of general Artificial intelligence, his work in Feature, Medical imaging, Image registration and Feature learning is often linked to Principles of learning, thereby combining diverse domains of study.
His study looks at the relationship between Pattern recognition and fields such as Artificial neural network, as well as how they intersect with chemical problems.
His Deep learning research includes elements of Image segmentation, Convolutional neural network, Curse of dimensionality and Feature vector.
The study incorporates disciplines such as Brain network and Neuroscience in addition to Disease.
His work on Missing data is typically connected to Consistency as part of general Machine learning study, connecting several disciplines of science.
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