World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
16975
World Ranking
6303
National Ranking
378

Overview

Daguang Xu is affiliated with Nvidia in the United Kingdom. Their research spans the intersection of computer science and medicine, with a strong focus on artificial intelligence and medical imaging.

The primary fields of study for Daguang Xu include:

  • Computer Science
  • Medicine

Their subfields of study consist of:

  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Computer Vision and Pattern Recognition
  • Pulmonary and Respiratory Medicine
  • Biomedical Engineering

Key research topics covered in their work include:

  • Radiomics and Machine Learning in Medical Imaging
  • Advanced Neural Network Applications
  • COVID-19 diagnosis using AI
  • Privacy-Preserving Technologies in Data
  • AI in cancer detection
  • Domain Adaptation and Few-Shot Learning
  • Medical Image Segmentation Techniques

Daguang Xu has contributed to several recent papers with publication dates mostly in 2022. Highlights include:

  • UNETR: Transformers for 3D Medical Image Segmentation, 2022, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • The Medical Segmentation Decathlon, 2022, Nature Communications
  • The Liver Tumor Segmentation Benchmark (LiTS), 2022, Medical Image Analysis
  • Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Federated learning for predicting clinical outcomes in patients with COVID-19, 2021, Nature Medicine

Frequent collaborators of Daguang Xu include:

  • Holger R. Roth
  • Dong Yang
  • Ziyue Xu
  • Andriy Myronenko
  • Wenqi Li

The consistent venues for their publications are:

  • arXiv (Cornell University)
  • Medical Image Analysis
  • IEEE Transactions on Medical Imaging
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • The Journal of Urology

Daguang Xu has also contributed book chapters published by Springer Science+Business Media. Titles associated with these contributions include:

  • Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning (2020)
  • Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health (2021)
  • Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning (2021)

Best Publications

  • The future of digital health with federated learning

    Nicola Rieke;Nicola Rieke;Jonny Hancox;Wenqi Li;Fausto Milletari

  • UNETR: Transformers for 3D Medical Image Segmentation

    Unknown

  • Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

    Unknown

  • The Liver Tumor Segmentation Benchmark (LiTS)

    Patrick Bilic;Patrick Ferdinand Christ;Eugene Vorontsov;Grzegorz Chlebus

  • The Medical Segmentation Decathlon

    Michela Antonelli;Annika Reinke;Spyridon Bakas;Keyvan Farahani

  • Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis

    Unknown

  • Federated learning for predicting clinical outcomes in patients with COVID-19.

    Ittai Dayan;Holger R. Roth;Aoxiao Zhong;Ahmed Harouni

  • Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.

    Stephanie A. Harmon;Thomas H. Sanford;Sheng Xu;Evrim B. Turkbey

  • Privacy-Preserving Federated Brain Tumour Segmentation

    Wenqi Li;Fausto Milletarì;Daguang Xu;Nicola Rieke

  • Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation

    Ling Zhang;Xiaosong Wang;Dong Yang;Thomas Sanford

  • Combo loss: Handling input and output imbalance in multi-organ segmentation

    Saeid Asgari Taghanaki;Saeid Asgari Taghanaki;Yefeng Zheng;S. Kevin Zhou;Bogdan Georgescu

  • VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

    Anjany Sekuboyina;Malek E. Husseini;Amirhossein Bayat;Maximilian Löffler

  • When Radiology Report Generation Meets Knowledge Graph

    Yixiao Zhang;Xiaosong Wang;Ziyue Xu;Qihang Yu

  • Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan.

    Dong Yang;Ziyue Xu;Wenqi Li;Andriy Myronenko

  • Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation

    Yingda Xia;Dong Yang;Zhiding Yu;Fengze Liu

  • UNETR: Transformers for 3D Medical Image Segmentation

    Ali Hatamizadeh;Dong Yang;Holger Roth;Daguang Xu

  • Federated learning improves site performance in multicenter deep learning without data sharing.

    Karthik V Sarma;Stephanie Harmon;Thomas Sanford;Holger R Roth

  • Automatic Liver Segmentation Using an Adversarial Image-to-Image Network

    Dong Yang;Daguang Xu;S. Kevin Zhou;Bogdan Georgescu

  • 3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training

    Yingda Xia;Fengze Liu;Dong Yang;Jinzheng Cai

  • Automatic Liver Segmentation Using Adversarial Image-to-Image Network

    Dong Yang;Daguang Xu;Shaohua Kevin Zhou;Bogdan Georgescu

  • C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation

    Qihang Yu;Dong Yang;Holger Roth;Yutong Bai

  • The Liver Tumor Segmentation Benchmark (LiTS)

    Unknown

  • Federated Learning for Breast Density Classification: A Real-World Implementation.

    Holger R. Roth;Ken Chang;Praveer Singh;Nir Neumark

  • Automatic Vertebra Labeling in Large-Scale 3D CT Using Deep Image-to-Image Network with Message Passing and Sparsity Regularization

    Dong Yang;Tao Xiong;Daguang Xu;Qiangui Huang

  • Method and System for Image Registration Using an Intelligent Artificial Agent

    Rui Liao;Shun Miao;Pierre de Tournemire;Julian Krebs

  • 3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes

    Siqi Liu;Daguang Xu;S. Kevin Zhou;Thomas Mertelmeier

Frequent Co-Authors

Holger R. Roth
Holger R. Roth Nvidia (United States)
Ziyue Xu
Ziyue Xu Nvidia (United States)
Dorin Comaniciu
Dorin Comaniciu Siemens (United States)
Alan L. Yuille
Alan L. Yuille Johns Hopkins University
Jin U. Kang
Jin U. Kang Johns Hopkins University
S. Kevin Zhou
S. Kevin Zhou University of Science and Technology of China
Yefeng Zheng
Yefeng Zheng Tencent (China)
Bogdan Georgescu
Bogdan Georgescu Princeton University
Bennett A. Landman
Bennett A. Landman Vanderbilt University
Klaus H. Maier-Hein
Klaus H. Maier-Hein German Cancer Research Center

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