World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
23923
World Ranking
3954
National Ranking
1879

Overview

Holger R. Roth is affiliated with Nvidia in the United States. Their research spans the intersection of computer science and medicine, focusing particularly on the development and application of artificial intelligence in medical imaging and healthcare.

The main fields of study covered in their work include:

  • Computer Science
  • Medicine

Their subfields of study show deep engagement with:

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

Holger R. Roth's research covers a variety of specialized topics such as:

  • 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
  • Artificial Intelligence in Healthcare and Education
  • Medical Image Segmentation Techniques

Frequent coauthors include:

  • Daguang Xu
  • Dong Yang
  • Ziyue Xu
  • Wenqi Li
  • Andriy Myronenko

Holger R. Roth has contributed extensively to academic literature, with recent papers such as:

  • UNETR: Transformers for 3D Medical Image Segmentation, 2022, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • 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
  • 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

Key publication venues for their work include:

  • arXiv (Cornell University)
  • IEEE Transactions on Medical Imaging
  • Medical Image Analysis
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Abdominal Radiology

Holger R. Roth has also contributed to book publications under Springer Science+Business Media, including titles such as:

  • Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning, 2020
  • Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning, 2021

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

  • 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

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

    Unknown

  • DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

    Holger R. Roth;Le Lu;Amal Farag;Hoo-Chang Shin

  • Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation

    Holger R. Roth;Le Lu;Jiamin Liu;Jianhua Yao

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

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

  • A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations.

    Holger R. Roth;Le Lu;Ari Seff;Kevin M. Cherry

  • 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

  • Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience

    Masashi Misawa;Shin-ei Kudo;Yuichi Mori;Tomonari Cho

  • Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation.

    Holger R. Roth;Le Lu;Nathan Lay;Adam P. Harrison

  • An application of cascaded 3D fully convolutional networks for medical image segmentation.

    Holger R. Roth;Hirohisa Oda;Xiangrong Zhou;Natsuki Shimizu

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

    Mingchen Gao;Ulas Bagci;Le Lu;Aaron Wu

  • 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

  • A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations

    Holger R. Roth;Le Lu;Ari Seff;Kevin M. Cherry

  • 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

  • Anatomy-specific classification of medical images using deep convolutional nets

    Holger R. Roth;Christopher T. Lee;Hoo-Chang Shin;Ari Seff

  • Deep convolutional networks for pancreas segmentation in CT imaging

    Holger R. Roth;Amal Farag;Le Lu;Evrim B. Turkbey

  • A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling

    Amal Farag;Le Lu;Holger R. Roth;Jiamin Liu

  • An application of cascaded 3D fully convolutional networks for medical image segmentation

    Holger R. Roth;Hirohisa Oda;Xiangrong Zhou;Natsuki Shimizu

  • Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation

    Holger R. Roth;Le Lu;Amal Farag;Andrew Sohn

  • Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation.

    Kao-Lang Liu;Tinghui Wu;Po-Ting Chen;Yuhsiang M Tsai

Frequent Co-Authors

Kensaku Mori
Kensaku Mori Nagoya University
Daguang Xu
Daguang Xu Nvidia (United Kingdom)
Ziyue Xu
Ziyue Xu Nvidia (United States)
Ronald M. Summers
Ronald M. Summers National Institutes of Health
Le Lu
Le Lu Alibaba Group (China)
David J. Hawkes
David J. Hawkes University College London
Jianhua Yao
Jianhua Yao Tencent (China)
Greg Slabaugh
Greg Slabaugh Queen Mary University of London
Sebastien Ourselin
Sebastien Ourselin King's College London
Shota Nakamura
Shota Nakamura Osaka University

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