World's Best Scientists 2026 revealed!
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Computer Science
China
2026

D-Index & Metrics

Computer Science

D-Index
146
Citations
81843
World Ranking
43
National Ranking
4

Research.com Recognitions

  • 2026 - Research.com Computer Science in China Leader Award
  • 2025 - Research.com Computer Science in China Leader Award
  • 2023 - Research.com Computer Science in China Leader Award
  • 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)

Overview

Dinggang Shen is affiliated with ShanghaiTech University in China and has a diverse research portfolio situated at the intersection of medicine and computer science. Their work primarily focuses on medical imaging, employing advanced computational methods such as machine learning and artificial intelligence to address challenges in diagnosis and disease quantification.

The scientist has been involved in research spanning multiple fields of study, including:

  • Medicine
  • Computer Science

Their research also extends into several specialized subfields, such as:

  • Radiology, Nuclear Medicine and Imaging
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Cognitive Neuroscience
  • Biomedical Engineering

The main topics of their work cover a range of applications in medical imaging and analysis:

  • Radiomics and Machine Learning in Medical Imaging
  • Medical Image Segmentation Techniques
  • Functional Brain Connectivity Studies
  • AI in cancer detection
  • Medical Imaging Techniques and Applications
  • Advanced Neuroimaging Techniques and Applications
  • Advanced MRI Techniques and Applications

Dinggang Shen has a substantial publication record, with frequent contributions to several notable venues in the domain of medical and imaging research:

  • UNC Libraries
  • arXiv (Cornell University)
  • IEEE Transactions on Medical Imaging
  • Medical Image Analysis
  • IEEE Journal of Biomedical and Health Informatics

Some of their recent papers include:

  • "Lung Infection Quantification of COVID-19 in CT Images with Deep Learning," 2020, arXiv (Cornell University)
  • "A deep learning system for detecting diabetic retinopathy across the disease spectrum," 2021, Nature Communications
  • "Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia," 2020, IEEE Transactions on Medical Imaging
  • "Transformers in medical image analysis," 2022, Intelligent Medicine
  • "Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images," 2020, Medical Image Analysis

The researcher has collaborated extensively with several frequent co-authors, which include:

  • Feng Shi
  • Weili Lin
  • Pew-Thian Yap
  • Li Wang
  • Qian Wang

Dinggang Shen has been recognized by professional organizations for their contributions in biomedical pattern recognition and medical image analysis. Their awards include:

  • Fellow of the International Association for Pattern Recognition (IAPR), 2018, for contributions to biomedical applications of pattern recognition and medical image analysis
  • Fellow of the Indian National Academy of Engineering (INAE), 2017

Best Publications

  • Deep Learning in Medical Image Analysis

    Dinggang Shen;Guorong Wu;Heung Il Suk

  • Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19

    Feng Shi;Jun Wang;Jun Shi;Ziyan Wu

  • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

    Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer

  • Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment

    Daoqiang Zhang;Yaping Wang;Luping Zhou;Hong Yuan

  • HAMMER: hierarchical attribute matching mechanism for elastic registration

    Dinggang Shen;C. Davatzikos

  • Lane detection and tracking using B-Snake

    Yue Wang;Eam Khwang Teoh;Dinggang Shen

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

    Heung-Il Suk;Seong-Whan Lee;Dinggang Shen

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

    Wenlu Zhang;Rongjian Li;Houtao Deng;Li Wang

  • Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

    Jie Zhi Cheng;Dong Ni;Yi Hong Chou;Jing Qin

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

    Daoqiang Zhang;Daoqiang Zhang;Dinggang Shen

  • Medical Image Synthesis with Context-Aware Generative Adversarial Networks

    Dong Nie;Roger Trullo;Jun Lian;Caroline Petitjean

  • Lung Infection Quantification of COVID-19 in CT Images with Deep Learning

    Fei Shan;Yaozong Gao;Jun Wang;Weiya Shi

  • Infant brain atlases from neonates to 1- and 2-year-olds.

    Feng Shi;Pew Thian Yap;Guorong Wu;Hongjun Jia

  • Medical Image Synthesis with Deep Convolutional Adversarial Networks

    Dong Nie;Roger Trullo;Jun Lian;Li Wang

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

    Christos Davatzikos;Kosha Ruparel;Yong Fan;Dinggang Shen

  • Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

    Heung Il Suk;Seong Whan Lee;Dinggang Shen;Dinggang Shen

  • Deep learning based imaging data completion for improved brain disease diagnosis.

    Rongjian Li;Wenlu Zhang;Heung Il Suk;Li Wang

  • A deep learning system for detecting diabetic retinopathy across the disease spectrum.

    Ling Dai;Liang Wu;Huating Li;Chun Cai

  • Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI

    Chunfeng Lian;Mingxia Liu;Jun Zhang;Dinggang Shen

  • Deep Learning-Based Feature Representation for AD/MCI Classification

    Heung Il Suk;Dinggang Shen

  • A Multi-Organ Nucleus Segmentation Challenge

    Neeraj Kumar;Ruchika Verma;Deepak Anand;Yanning Zhou

  • Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

    Christos Davatzikos;Yong Fan;Xiaoying Wu;Dinggang Shen

  • State-space model with deep learning for functional dynamics estimation in resting-state fMRI

    Heung Il Suk;Chong Yaw Wee;Seong Whan Lee;Dinggang Shen

  • Medical Image Synthesis with Context-Aware Generative Adversarial Networks

    Dong Nie;Roger Trullo;Caroline Petitjean;Su Ruan

  • Machine learning in medical imaging

    Pingkun Yan;Kenji Suzuki;Fei Wang;Dinggang Shen

  • Editorial: machine learning in medical imaging

    Kenji Suzuki;Pingkun Yan;Fei Wang;Dinggang Shen

Frequent Co-Authors

Pew Thian Yap
Pew Thian Yap University of North Carolina at Chapel Hill
Guorong Wu
Guorong Wu University of North Carolina at Chapel Hill
Gang Li
Gang Li University of North Carolina at Chapel Hill
Qian Wang
Qian Wang Shanghai Jiao Tong University
Feng Shi
Feng Shi United Imaging Intelligence (China)
Yaozong Gao
Yaozong Gao United Imaging Healthcare (China)
Christos Davatzikos
Christos Davatzikos University of Pennsylvania
Daoqiang Zhang
Daoqiang Zhang Nanjing University of Aeronautics and Astronautics
Han Zhang
Han Zhang ShanghaiTech University
Mingxia Liu
Mingxia Liu University of North Carolina at Chapel Hill

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