What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Magnetic resonance imaging
Artificial intelligence, Pattern recognition, Computer vision, Magnetic resonance imaging and Segmentation are his primary areas of study.
His Artificial intelligence research integrates issues from Machine learning and Neuroimaging.
His studies deal with areas such as Modality and Correlation as well as Pattern recognition.
His Magnetic resonance imaging study also includes fields such as
- Brain mapping which is related to area like White matter and Cerebral cortex,
- Atlas that intertwine with fields like Spatial normalization.
His studies in Segmentation integrate themes in fields like Image processing, Partial volume and Image quality.
He works mostly in the field of Deep learning, limiting it down to concerns involving Convolutional neural network and, occasionally, Mr images.
His most cited work include:
- Deep Learning in Medical Image Analysis (1360 citations)
- HAMMER: hierarchical attribute matching mechanism for elastic registration (934 citations)
- Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment (792 citations)
What are the main themes of his work throughout his whole career to date?
Dinggang Shen mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Segmentation and Machine learning.
Dinggang Shen has included themes like Magnetic resonance imaging and Neuroimaging in his Artificial intelligence study.
His Pattern recognition study frequently draws connections between adjacent fields such as Feature.
Dinggang Shen combines subjects such as Sparse approximation, Mr images and Atlas with his study of Computer vision.
His research in Segmentation intersects with topics in Random forest and Medical imaging.
His work deals with themes such as Classifier, Functional magnetic resonance imaging and Cognitive impairment, which intersect with Machine learning.
He most often published in these fields:
- Artificial intelligence (97.41%)
- Pattern recognition (60.76%)
- Computer vision (29.78%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial intelligence (97.41%)
- Pattern recognition (60.76%)
- Segmentation (24.86%)
In recent papers he was focusing on the following fields of study:
Dinggang Shen mostly deals with Artificial intelligence, Pattern recognition, Segmentation, Feature and Deep learning.
The study of Artificial intelligence is intertwined with the study of Machine learning in a number of ways.
His Pattern recognition study combines topics from a wide range of disciplines, such as Disease progression, Identification, Modality, Image and Robustness.
His Segmentation research incorporates themes from Voxel, Radiology, Computed tomography, Medical imaging and Breast ultrasound.
His biological study spans a wide range of topics, including Surface and Benchmark.
He focuses mostly in the field of Deep learning, narrowing it down to matters related to Convolutional neural network and, in some cases, Polygon mesh, Histological diagnosis and Glioma.
Between 2020 and 2021, his most popular works were:
- Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 (297 citations)
- Large-scale screening of COVID-19 from community acquired pneumonia using infection size-aware classification. (35 citations)
- Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan. (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His main research concerns Artificial intelligence, Pattern recognition, Segmentation, Severity assessment and Retrospective cohort study.
His Artificial intelligence research includes elements of Scale and Graph.
His work carried out in the field of Pattern recognition brings together such families of science as Disease progression, Diffusion MRI, Robustness and Vertex.
His Segmentation study deals with Computed tomography intersecting with Lung.
As a member of one scientific family, he mostly works in the field of Retrospective cohort study, focusing on Random forest and, on occasion, Tomography, Radiography, Scale, Generalizability theory and Image processing.
His study in Medical imaging is interdisciplinary in nature, drawing from both Machine learning and Image acquisition.
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