H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 116 Citations 52,645 1,140 World Ranking 62 National Ranking 7

Research.com Recognitions

Awards & Achievements

2018 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to biomedical applications of pattern recognition and medical image analysis

2017 - Fellow of the Indian National Academy of Engineering (INAE)


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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Deep Learning in Medical Image Analysis

Dinggang Shen;Guorong Wu;Heung Il Suk.
Annual Review of Biomedical Engineering (2017)

1257 Citations

HAMMER: hierarchical attribute matching mechanism for elastic registration

Dinggang Shen;C. Davatzikos.
IEEE Transactions on Medical Imaging (2002)

1128 Citations

Lane detection and tracking using B-Snake

Yue Wang;Eam Khwang Teoh;Dinggang Shen.
Image and Vision Computing (2004)

1011 Citations

Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment

Daoqiang Zhang;Yaping Wang;Luping Zhou;Hong Yuan.
NeuroImage (2011)

961 Citations

Deep convolutional neural networks for multi-modality isointense infant brain image segmentation.

Wenlu Zhang;Rongjian Li;Houtao Deng;Li Wang.
NeuroImage (2015)

532 Citations

Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection

Christos Davatzikos;Kosha Ruparel;Yong Fan;Dinggang Shen.
NeuroImage (2005)

524 Citations

Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.

Heung-Il Suk;Seong-Whan Lee;Dinggang Shen.
NeuroImage (2014)

481 Citations

Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease

Daoqiang Zhang;Daoqiang Zhang;Dinggang Shen.
NeuroImage (2012)

472 Citations

Evidence on the emergence of the brain's default network from 2-week-old to 2-year-old healthy pediatric subjects

Wei Gao;Hongtu Zhu;Kelly S. Giovanello;J. Keith Smith.
Proceedings of the National Academy of Sciences of the United States of America (2009)

452 Citations

Longitudinal pattern of regional brain volume change differentiates normal aging from MCI

I. Driscoll;C. Davatzikos;Y. An;X. Wu.
Neurology (2009)

431 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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