2022 - Research.com Computer Science in China Leader Award
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)
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
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.
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.
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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Deep Learning in Medical Image Analysis
Dinggang Shen;Guorong Wu;Heung Il Suk.
Annual Review of Biomedical Engineering (2017)
HAMMER: hierarchical attribute matching mechanism for elastic registration
Dinggang Shen;C. Davatzikos.
IEEE Transactions on Medical Imaging (2002)
Lane detection and tracking using B-Snake
Yue Wang;Eam Khwang Teoh;Dinggang Shen.
Image and Vision Computing (2004)
Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment
Daoqiang Zhang;Yaping Wang;Luping Zhou;Hong Yuan.
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation.
Wenlu Zhang;Rongjian Li;Houtao Deng;Li Wang.
Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection
Christos Davatzikos;Kosha Ruparel;Yong Fan;Dinggang Shen.
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.
Heung-Il Suk;Seong-Whan Lee;Dinggang Shen.
Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease
Daoqiang Zhang;Daoqiang Zhang;Dinggang Shen.
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)
Longitudinal pattern of regional brain volume change differentiates normal aging from MCI
I. Driscoll;C. Davatzikos;Y. An;X. Wu.
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