World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
12142
World Ranking
9968
National Ranking
4194

Overview

Ziyue Xu is affiliated with Nvidia in the United States and has a prolific academic record spanning multiple areas within computer science and medicine. Their research primarily explores the intersection of artificial intelligence and medical imaging, focusing on topics such as radiomics, machine learning, and applications of AI in disease diagnosis and treatment.

They have published extensively, with key recent papers including:

  • Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets (2020, Nature Communications)
  • Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation (2020, IEEE Transactions on Medical Imaging)
  • When Radiology Report Generation Meets Knowledge Graph (2020, Proceedings of the AAAI Conference on Artificial Intelligence)
  • Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan (2021, Medical Image Analysis)
  • Federated learning improves site performance in multicenter deep learning without data sharing (2020, Journal of the American Medical Informatics Association)

Xu collaborates frequently with several researchers, including:

  • Daguang Xu (59 joint publications)
  • Holger R. Roth (39 joint publications)
  • Dong Yang (37 joint publications)
  • Bradford J. Wood (26 joint publications)
  • Barış Türkbey (26 joint publications)

Their publications are commonly found in notable venues such as:

  • arXiv (Cornell University) with 25 publications
  • Medical Image Analysis with 3 publications
  • Academic Radiology with 3 publications
  • Abdominal Radiology with 3 publications
  • The Journal of Urology with 3 publications

Xu has contributed chapters to several books published by Springer Science+Business Media, including works on domain adaptation, representation transfer, distributed and collaborative learning, and applications of AI in affordable healthcare and global health.

Their main fields of study include computer science with 108 publications and medicine with 91 publications. Subfields of particular focus comprise:

  • Artificial Intelligence (55 publications)
  • Computer Vision and Pattern Recognition (52 publications)
  • Radiology, Nuclear Medicine and Imaging (51 publications)
  • Pulmonary and Respiratory Medicine (24 publications)
  • Molecular Biology (12 publications)

The primary research topics pursued by Xu are:

  • Radiomics and Machine Learning in Medical Imaging (54 publications)
  • Advanced Neural Network Applications (40 publications)
  • COVID-19 Diagnosis Using AI (36 publications)
  • AI in Cancer Detection (28 publications)
  • Prostate Cancer Diagnosis and Treatment (26 publications)
  • Privacy-Preserving Technologies in Data (24 publications)
  • Medical Image Segmentation Techniques (24 publications)

Best Publications

  • Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

    Hoo-Chang Shin;Holger R. Roth;Mingchen Gao;Le Lu

  • 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

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

    Ling Zhang;Xiaosong Wang;Dong Yang;Thomas Sanford

  • A review on segmentation of positron emission tomography images

    Brent Foster;Ulas Bagci;Awais Mansoor;Ziyue Xu

  • Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends

    Awais Mansoor;Ulas Bagci;Brent Foster;Ziyue Xu

  • Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

    Hugo J. Kuijf;Adria Casamitjana;D. Louis Collins;Mahsa Dadar

  • Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks.

    Mingchen Gao;Ulas Bagci;Le Lu;Aaron Wu

  • 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

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

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

  • A Generic Approach to Pathological Lung Segmentation

    Awais Mansoor;Ulas Bagci;Ziyue Xu;Brent Foster

  • Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images

    Ulas Bagci;Jayaram K. Udupa;Neil Mendhiratta;Neil Mendhiratta;Brent Foster

  • CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation

    Dakai Jin;Ziyue Xu;Youbao Tang;Adam P. Harrison

  • Progressive and Multi-path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images

    Adam P. Harrison;Ziyue Xu;Kevin George;Le Lu

  • Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

    Hugo J. Kuijf;J. Matthijs Biesbroek;Jeroen de Bresser;Rutger Heinen

  • Deep vessel tracking: A generalized probabilistic approach via deep learning

    Aaron Wu;Ziyue Xu;Mingchen Gao;Mario Buty

  • Capsules for biomedical image segmentation.

    Rodney LaLonde;Ziyue Xu;Ismail Irmakci;Sanjay Jain

  • Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation

    Unknown

  • Segmentation of PET Images for Computer-Aided Functional Quantification of Tuberculosis in Small Animal Models

    Brent Foster;Ulas Bagci;Ziyue Xu;Bappaditya Dey

  • Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

    Unknown

  • 3D Convolutional Neural Networks with Graph Refinement for Airway Segmentation Using Incomplete Data Labels

    Dakai Jin;Ziyue Xu;Adam P. Harrison;Kevin George

  • A Cascaded Deep Learning-Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging.

    Sherif Mehralivand;Dong Yang;Stephanie A. Harmon;Daguang Xu

  • Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images.

    Maxime Blain;Michael T. Kassin;Nicole Varble;Xiaosong Wang

  • Interactive segmentation of medical images through fully convolutional neural networks.

    Tomas Sakinis;Fausto Milletari;Holger Roth;Panagiotis Korfiatis

Frequent Co-Authors

Daguang Xu
Daguang Xu Nvidia (United Kingdom)
Holger R. Roth
Holger R. Roth Nvidia (United States)
Ulas Bagci
Ulas Bagci Northwestern University
Ronald M. Summers
Ronald M. Summers National Institutes of Health
Le Lu
Le Lu Alibaba Group (China)
Jayaram K. Udupa
Jayaram K. Udupa University of Pennsylvania
William R. Bishai
William R. Bishai Johns Hopkins University
Punam K. Saha
Punam K. Saha University of Iowa
Colleen B. Jonsson
Colleen B. Jonsson University of Tennessee Health Science Center
Jianhua Yao
Jianhua Yao Tencent (China)

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