Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Daguang Xu

Daguang Xu

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


Jan 04, 2022
Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger Roth, Daguang Xu

Figure 1 for Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
Figure 2 for Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
Figure 3 for Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
Figure 4 for Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
* 13 pages, 3 figures 
  Access Paper or Ask Questions

HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet


Dec 20, 2021
Cheng Peng, Andriy Myronenko, Ali Hatamizadeh, Vish Nath, Md Mahfuzur Rahman Siddiquee, Yufan He, Daguang Xu, Rama Chellappa, Dong Yang

Figure 1 for HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet
Figure 2 for HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet
Figure 3 for HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet
Figure 4 for HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet

  Access Paper or Ask Questions

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


Nov 29, 2021
Yucheng Tang, Dong Yang, Wenqi Li, Holger Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, Ali Hatamizadeh

Figure 1 for Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
Figure 2 for Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
Figure 3 for Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
Figure 4 for Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
* 16 pages, 9 figures 
  Access Paper or Ask Questions

T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging


Nov 15, 2021
Dong Yang, Andriy Myronenko, Xiaosong Wang, Ziyue Xu, Holger R. Roth, Daguang Xu

Figure 1 for T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging
Figure 2 for T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging
Figure 3 for T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging
Figure 4 for T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging
* Accepted at ICCV 2021 
  Access Paper or Ask Questions

Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging


Nov 01, 2021
Andriy Myronenko, Ziyue Xu, Dong Yang, Holger Roth, Daguang Xu

Figure 1 for Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging
Figure 2 for Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging
Figure 3 for Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging
Figure 4 for Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging
* MICCAI 2021 
  Access Paper or Ask Questions

MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation


Oct 08, 2021
Alexandros Karargyris, Renato Umeton, Micah J. Sheller, Alejandro Aristizabal, Johnu George, Srini Bala, Daniel J. Beutel, Victor Bittorf, Akshay Chaudhari, Alexander Chowdhury, Cody Coleman, Bala Desinghu, Gregory Diamos, Debo Dutta, Diane Feddema, Grigori Fursin, Junyi Guo, Xinyuan Huang, David Kanter, Satyananda Kashyap, Nicholas Lane, Indranil Mallick, Pietro Mascagni, Virendra Mehta, Vivek Natarajan, Nikola Nikolov, Nicolas Padoy, Gennady Pekhimenko, Vijay Janapa Reddi, G Anthony Reina, Pablo Ribalta, Jacob Rosenthal, Abhishek Singh, Jayaraman J. Thiagarajan, Anna Wuest, Maria Xenochristou, Daguang Xu, Poonam Yadav, Michael Rosenthal, Massimo Loda, Jason M. Johnson, Peter Mattson

Figure 1 for MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Figure 2 for MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Figure 3 for MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Figure 4 for MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation

  Access Paper or Ask Questions

Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports


Oct 06, 2021
Riddhish Bhalodia, Ali Hatamizadeh, Leo Tam, Ziyue Xu, Xiaosong Wang, Evrim Turkbey, Daguang Xu

Figure 1 for Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports
Figure 2 for Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports
Figure 3 for Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports
Figure 4 for Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports
* Published at MICCAI 2021 
  Access Paper or Ask Questions

Multi-task Federated Learning for Heterogeneous Pancreas Segmentation


Aug 19, 2021
Chen Shen, Pochuan Wang, Holger R. Roth, Dong Yang, Daguang Xu, Masahiro Oda, Weichung Wang, Chiou-Shann Fuh, Po-Ting Chen, Kao-Lang Liu, Wei-Chih Liao, Kensaku Mori

Figure 1 for Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
Figure 2 for Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
Figure 3 for Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
Figure 4 for Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
* Accepted by MICCAI DCL Workshop 2021 
  Access Paper or Ask Questions

Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures


Jul 16, 2021
Holger R. Roth, Dong Yang, Wenqi Li, Andriy Myronenko, Wentao Zhu, Ziyue Xu, Xiaosong Wang, Daguang Xu

Figure 1 for Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures
Figure 2 for Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures
Figure 3 for Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures
* MICCAI 2021 accepted 
  Access Paper or Ask Questions

The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization in Medical Image Segmentation


Jul 12, 2021
Vishwesh Nath, Dong Yang, Ali Hatamizadeh, Anas A. Abidin, Andriy Myronenko, Holger Roth, Daguang Xu

Figure 1 for The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization in Medical Image Segmentation
Figure 2 for The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization in Medical Image Segmentation
Figure 3 for The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization in Medical Image Segmentation
Figure 4 for The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization in Medical Image Segmentation

  Access Paper or Ask Questions

The Medical Segmentation Decathlon


Jun 10, 2021
Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani, AnnetteKopp-Schneider, Bennett A. Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M. Summers, Bram van Ginneken, Michel Bilello, Patrick Bilic, Patrick F. Christ, Richard K. G. Do, Marc J. Gollub, Stephan H. Heckers, Henkjan Huisman, William R. Jarnagin, Maureen K. McHugo, Sandy Napel, Jennifer S. Goli Pernicka, Kawal Rhode, Catalina Tobon-Gomez, Eugene Vorontsov, Henkjan Huisman, James A. Meakin, Sebastien Ourselin, Manuel Wiesenfarth, Pablo Arbelaez, Byeonguk Bae, Sihong Chen, Laura Daza, Jianjiang Feng, Baochun He, Fabian Isensee, Yuanfeng Ji, Fucang Jia, Namkug Kim, Ildoo Kim, Dorit Merhof, Akshay Pai, Beomhee Park, Mathias Perslev, Ramin Rezaiifar, Oliver Rippel, Ignacio Sarasua, Wei Shen, Jaemin Son, Christian Wachinger, Liansheng Wang, Yan Wang, Yingda Xia, Daguang Xu, Zhanwei Xu, Yefeng Zheng, Amber L. Simpson, Lena Maier-Hein, M. Jorge Cardoso

Figure 1 for The Medical Segmentation Decathlon
Figure 2 for The Medical Segmentation Decathlon
Figure 3 for The Medical Segmentation Decathlon
Figure 4 for The Medical Segmentation Decathlon

  Access Paper or Ask Questions

Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation


Apr 20, 2021
Yingda Xia, Dong Yang, Wenqi Li, Andriy Myronenko, Daguang Xu, Hirofumi Obinata, Hitoshi Mori, Peng An, Stephanie Harmon, Evrim Turkbey, Baris Turkbey, Bradford Wood, Francesca Patella, Elvira Stellato, Gianpaolo Carrafiello, Anna Ierardi, Alan Yuille, Holger Roth

Figure 1 for Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation
Figure 2 for Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation
Figure 3 for Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation
Figure 4 for Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation

  Access Paper or Ask Questions

Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays


Mar 30, 2021
Xiaosong Wang, Ziyue Xu, Leo Tam, Dong Yang, Daguang Xu

Figure 1 for Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays
Figure 2 for Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays
Figure 3 for Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays
Figure 4 for Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays

  Access Paper or Ask Questions

DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation


Mar 29, 2021
Yufan He, Dong Yang, Holger Roth, Can Zhao, Daguang Xu

Figure 1 for DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
Figure 2 for DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
Figure 3 for DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
Figure 4 for DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
* CVPR2021 oral 
  Access Paper or Ask Questions

Test-Time Training for Deformable Multi-Scale Image Registration


Mar 25, 2021
Wentao Zhu, Yufang Huang, Daguang Xu, Zhen Qian, Wei Fan, Xiaohui Xie

Figure 1 for Test-Time Training for Deformable Multi-Scale Image Registration
Figure 2 for Test-Time Training for Deformable Multi-Scale Image Registration
Figure 3 for Test-Time Training for Deformable Multi-Scale Image Registration
Figure 4 for Test-Time Training for Deformable Multi-Scale Image Registration
* ICRA 2021 
 * ICRA 2021; 8 pages, 4 figures, 2 big tables 
  Access Paper or Ask Questions

UNETR: Transformers for 3D Medical Image Segmentation


Mar 18, 2021
Ali Hatamizadeh, Dong Yang, Holger Roth, Daguang Xu

Figure 1 for UNETR: Transformers for 3D Medical Image Segmentation
Figure 2 for UNETR: Transformers for 3D Medical Image Segmentation
Figure 3 for UNETR: Transformers for 3D Medical Image Segmentation
Figure 4 for UNETR: Transformers for 3D Medical Image Segmentation
* 11 pages, 2 figures 
  Access Paper or Ask Questions

Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation


Jan 07, 2021
Vishwesh Nath, Dong Yang, Bennett A. Landman, Daguang Xu, Holger R. Roth

Figure 1 for Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation
Figure 2 for Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation
Figure 3 for Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation
Figure 4 for Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation
* IEEE Transactions on Medical Imaging, 2020 
 * 19 pages, 13 figures, Transactions of Medical Imaging 
  Access Paper or Ask Questions

Transformer Query-Target Knowledge Discovery (TEND): Drug Discovery from CORD-19


Dec 11, 2020
Leo K. Tam, Xiaosong Wang, Daguang Xu

Figure 1 for Transformer Query-Target Knowledge Discovery (TEND): Drug Discovery from CORD-19
Figure 2 for Transformer Query-Target Knowledge Discovery (TEND): Drug Discovery from CORD-19
Figure 3 for Transformer Query-Target Knowledge Discovery (TEND): Drug Discovery from CORD-19
Figure 4 for Transformer Query-Target Knowledge Discovery (TEND): Drug Discovery from CORD-19

  Access Paper or Ask Questions

Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan


Nov 23, 2020
Dong Yang, Ziyue Xu, Wenqi Li, Andriy Myronenko, Holger R. Roth, Stephanie Harmon, Sheng Xu, Baris Turkbey, Evrim Turkbey, Xiaosong Wang, Wentao Zhu, Gianpaolo Carrafiello, Francesca Patella, Maurizio Cariati, Hirofumi Obinata, Hitoshi Mori, Kaku Tamura, Peng An, Bradford J. Wood, Daguang Xu

Figure 1 for Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
Figure 2 for Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
Figure 3 for Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
Figure 4 for Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
* Accepted with minor revision to Medical Image Analysis 
  Access Paper or Ask Questions

Learning Image Labels On-the-fly for Training Robust Classification Models


Oct 02, 2020
Xiaosong Wang, Ziyue Xu, Dong Yang, Leo Tam, Holger Roth, Daguang Xu

Figure 1 for Learning Image Labels On-the-fly for Training Robust Classification Models
Figure 2 for Learning Image Labels On-the-fly for Training Robust Classification Models
Figure 3 for Learning Image Labels On-the-fly for Training Robust Classification Models
Figure 4 for Learning Image Labels On-the-fly for Training Robust Classification Models
* v2: Minor Corrections 
  Access Paper or Ask Questions

Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning


Sep 28, 2020
Pochuan Wang, Chen Shen, Holger R. Roth, Dong Yang, Daguang Xu, Masahiro Oda, Kazunari Misawa, Po-Ting Chen, Kao-Lang Liu, Wei-Chih Liao, Weichung Wang, Kensaku Mori

Figure 1 for Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
Figure 2 for Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
Figure 3 for Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
Figure 4 for Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
* Accepted by MICCAI DCL Workshop 2020 
  Access Paper or Ask Questions

Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning


Sep 25, 2020
Vikash Gupta1, Holger Roth, Varun Buch3, Marcio A. B. C. Rockenbach, Richard D White, Dong Yang, Olga Laur, Brian Ghoshhajra, Ittai Dayan, Daguang Xu, Mona G. Flores, Barbaros Selnur Erdal

Figure 1 for Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning
Figure 2 for Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning
Figure 3 for Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning
Figure 4 for Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning
* 8 pages, 5 figures, pre-print 
  Access Paper or Ask Questions

Going to Extremes: Weakly Supervised Medical Image Segmentation


Sep 25, 2020
Holger R Roth, Dong Yang, Ziyue Xu, Xiaosong Wang, Daguang Xu

Figure 1 for Going to Extremes: Weakly Supervised Medical Image Segmentation
Figure 2 for Going to Extremes: Weakly Supervised Medical Image Segmentation
Figure 3 for Going to Extremes: Weakly Supervised Medical Image Segmentation
Figure 4 for Going to Extremes: Weakly Supervised Medical Image Segmentation
* 13 pages, 6 figures, 1 table 
  Access Paper or Ask Questions

Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies


Sep 23, 2020
Haomin Chen, Shun Miao, Daguang Xu, Gregory D. Hager, Adam P. Harrison

Figure 1 for Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies
Figure 2 for Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies
Figure 3 for Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies
Figure 4 for Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies
* MEDIMA 101811, 5 September 2020 
  Access Paper or Ask Questions

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


Sep 17, 2020
Holger R. Roth, Ken Chang, Praveer Singh, Nir Neumark, Wenqi Li, Vikash Gupta, Sharut Gupta, Liangqiong Qu, Alvin Ihsani, Bernardo C. Bizzo, Yuhong Wen, Varun Buch, Meesam Shah, Felipe Kitamura, Matheus Mendonça, Vitor Lavor, Ahmed Harouni, Colin Compas, Jesse Tetreault, Prerna Dogra, Yan Cheng, Selnur Erdal, Richard White, Behrooz Hashemian, Thomas Schultz, Miao Zhang, Adam McCarthy, B. Min Yun, Elshaimaa Sharaf, Katharina V. Hoebel, Jay B. Patel, Bryan Chen, Sean Ko, Evan Leibovitz, Etta D. Pisano, Laura Coombs, Daguang Xu, Keith J. Dreyer, Ittai Dayan, Ram C. Naidu, Mona Flores, Daniel Rubin, Jayashree Kalpathy-Cramer

Figure 1 for Federated Learning for Breast Density Classification: A Real-World Implementation
Figure 2 for Federated Learning for Breast Density Classification: A Real-World Implementation
Figure 3 for Federated Learning for Breast Density Classification: A Real-World Implementation
Figure 4 for Federated Learning for Breast Density Classification: A Real-World Implementation
* Accepted at the 1st MICCAI Workshop on "Distributed And Collaborative Learning"; add citation to Fig. 1 & 2 and update Fig. 5 
  Access Paper or Ask Questions

Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies


Jul 31, 2020
Leo K. Tam, Xiaosong Wang, Evrim Turkbey, Kevin Lu, Yuhong Wen, Daguang Xu

Figure 1 for Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies
Figure 2 for Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies
Figure 3 for Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies
Figure 4 for Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies
* Accepted at Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2020 
  Access Paper or Ask Questions

Multi-Domain Image Completion for Random Missing Input Data


Jul 10, 2020
Liyue Shen, Wentao Zhu, Xiaosong Wang, Lei Xing, John M. Pauly, Baris Turkbey, Stephanie Anne Harmon, Thomas Hogue Sanford, Sherif Mehralivand, Peter Choyke, Bradford Wood, Daguang Xu

Figure 1 for Multi-Domain Image Completion for Random Missing Input Data
Figure 2 for Multi-Domain Image Completion for Random Missing Input Data
Figure 3 for Multi-Domain Image Completion for Random Missing Input Data
Figure 4 for Multi-Domain Image Completion for Random Missing Input Data

  Access Paper or Ask Questions