H. Roth

发表

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

Ronald M. Summers, Jiamin Liu, Holger Roth, 2014, MICCAI.

Fully Convolutional Network-Based Eyeball Segmentation from Sparse Annotation for Eye Surgery Simulation Model

Holger Roth, Masahiro Oda, Kensaku Mori, 2018, POCUS/BIVPCS/CuRIOUS/CPM@MICCAI.

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

Mona G. Flores, P. Choyke, U. Bagci, 2020, Nature Communications.

Attention-Guided Pancreatic Duct Segmentation from Abdominal CT Volumes

K. Mori, H. Roth, M. Oda, 2021, Lecture Notes in Computer Science.

The future of digital health with federated learning.

Micah J. Sheller, Ronald M. Summers, Shadi Albarqouni, 2020, NPJ digital medicine.

The future of digital health with federated learning

Micah J. Sheller, Klaus H. Maier-Hein, Ronald M. Summers, 2020, npj Digital Medicine.

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

Daguang Xu, Bennett A Landman, Vishwesh Nath, 2020, IEEE Transactions on Medical Imaging.

Federated Learning used for predicting outcomes in SARS-COV-2 patients

Colin B. Compas, Yu Rim Lee, Mona G. Flores, 2021, Research square.

Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge

Klaus H. Maier-Hein, Mona G. Flores, V. Kovalev, 2021, Research square.

Title Detection of sclerotic spine metastases via random aggregation of deep convolutional neural network classifications Permalink

Joseph E. Burns, H. Roth, Le Lu, 2014 .

Interactive segmentation of medical images through fully convolutional neural networks pdf

Daguang Xu, Holger Roth, Bradley J. Erickson, 2019, ArXiv.

Cardiac fiber tracking on super high-resolution CT images: a comparative study

Tetsuya Yuasa, Hirohisa Oda, Masahiro Oda, 2020, Journal of medical imaging.

Unsupervised segmentation of 3D medical images based on clustering and deep representation learning

Holger Roth, Hirohisa Oda, Masahiro Oda, 2018, Medical Imaging.

Technical and Clinical Factors Affecting Success Rate of a Deep Learning Method for Pancreas Segmentation on CT.

H. Roth, R. Summers, Jianhua Yao, 2020, Academic radiology.

External Clinical Validation of Prone and Supine CT Colonography Registration

David J. Hawkes, Holger Roth, Thomas Hampshire, 2012, Abdominal Imaging.

Registration of prone and supine CT colonography images and its clinical application

H. Roth, 2013 .

Lung segmentation based on a deep learning approach for dynamic chest radiography

Holger Roth, Hirohisa Oda, Kensaku Mori, 2019, Medical Imaging.

The future of digital health with federated learning

Ronald M. Summers, Daguang Xu, M. Jorge Cardoso, 2020, npj Digital Medicine.

Precision Medicine in Pancreatic Disease—Knowledge Gaps and Research Opportunities

S. Joshua Swamidass, J. Gray, S. Plevritis, 2019, Pancreas.

Guest Editorial Annotation-Efficient Deep Learning: The Holy Grail of Medical Imaging

Nima Tajbakhsh, Holger Roth, Jianming Liang, 2021, IEEE Trans. Medical Imaging.

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

Daguang Xu, Jesse Tetreault, Ahmed Harouni, 2020, DART/DCL@MICCAI.

Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively

Kensaku Mori, Jiang Yu, Xingyu Feng, 2018, European Radiology.

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

Yuhsiang M. Tsai, Wei-Chih Liao, H. Roth, 2020, The Lancet. Digital health.

Detection and Classification of Coronary Artery Plaques in Coronary Computed Tomography Angiography Using 3D CNN

Holger Roth, Cheng-Ying Chou, Dong Yang, 2021, STACOM@MICCAI.

The UCL Centre for Medical Image Computing (CMIC): An integrated suite of open source software tools to support imaging research in Neurosciences, Oncology and Cardiovascular Sciences

M. Jorge Cardoso, Marc Modat, Lianghao Han, 2011 .

Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation

Vishal M. Patel, S. Harmon, H. Roth, 2022, ECCV.

Rapid artificial intelligence solutions in a pandemic—The COVID-19-20 Lung CT Lesion Segmentation Challenge

Klaus H. Maier-Hein, Mona G. Flores, V. Kovalev, 2022, Medical Image Analysis.

Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation

Ziyue Xu, H. Roth, A. Myronenko, 2022, DeCaF/FAIR@MICCAI.

Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study.

Wei-Chih Liao, H. Roth, Ming-Shiang Wu, 2022, Radiology.

Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge

Víctor M. Campello, D. Rueckert, Maxime Sermesant, 2020, Medical Image Anal..

Rapid artificial intelligence solutions in a pandemic—The COVID-19-20 Lung CT Lesion Segmentation Challenge

Klaus H. Maier-Hein, Mona G. Flores, V. Kovalev, 2022, Medical Image Analysis.

NVIDIA FLARE: Federated Learning from Simulation to Real-World

Sean Bin Yang, Mona G. Flores, Ziyue Xu, 2022, ArXiv.

MONAI: An open-source framework for deep learning in healthcare

Mohammad Zalbagi Darestani, Paul F. Jaeger, Brad W. Genereaux, 2022, ArXiv.

Data Augmentation and Transfer Learning to Improve Generalizability of an Automated Prostate Segmentation Model.

P. Choyke, Ziyue Xu, A. Oto, 2020, AJR. American journal of roentgenology.

Warm Start Active Learning with Proxy Labels and Selection via Semi-supervised Fine-Tuning

H. Roth, Daguang Xu, Dong Yang, 2022, MICCAI.

Rapid artificial intelligence solutions in a pandemic—The COVID-19-20 Lung CT Lesion Segmentation Challenge

Mona G. Flores, V. Kovalev, V. Liauchuk, 2022, Medical image analysis.

NVIDIA FLARE: Federated Learning from Simulation to Real-World

Sean Bin Yang, Mona G. Flores, Ziyue Xu, 2022, ArXiv.

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

A. Yuille, H. Roth, Qihang Yu, 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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

Dong Yang, Daguang Xu, Lequan Yu, 2020, Medical Image Anal..

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

Daguang Xu, Bradford J. Wood, Ling Zhang, 2020, IEEE Transactions on Medical Imaging.

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

Ronald M. Summers, Jianhua Yao, Mingchen Gao, 2016, IEEE Transactions on Medical Imaging.

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

Ronald M. Summers, Ulas Bagci, Holger Roth, 2018, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

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

Bradford J. Wood, Xiaosong Wang, Dong Yang, 2020, Medical Image Analysis.

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

Holger Roth, Vishwesh Nath, Daguang Xu, 2022, BrainLes@MICCAI.

GradViT: Gradient Inversion of Vision Transformers

J. Kautz, H. Roth, Pavlo Molchanov, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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

Colin B. Compas, Mona G. Flores, Xihong Lin, 2021, Nature Medicine.

Dense volumetric detection and segmentation of mediastinal lymph nodes in chest CT images

Shingo Iwano, Holger Roth, Hirohisa Oda, 2018, Medical Imaging.

UNETR: Transformers for 3D Medical Image Segmentation pdf

Daguang Xu, Holger Roth, Ali Hatamizadeh, 2021, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

Radiomic Features at CT Can Distinguish Pancreatic Cancer from Noncancerous Pancreas.

Wei-Chih Liao, H. Roth, Ming-Shiang Wu, 2021, Radiology. Imaging cancer.

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

Ronald M. Summers, Jiamin Liu, Holger Roth, 2015, MICCAI.

Deep convolutional networks for pancreas segmentation in CT imaging

Ronald M. Summers, Holger Roth, Le Lu, 2015, Medical Imaging.

An analysis of robust cost functions for CNN in computer-aided diagnosis

Ronald M. Summers, Adrian Barbu, Holger Roth, 2018, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

Weakly supervised segmentation from extreme points

Ziyue Xu, Ling Zhang, H. Roth, 2019, LABELS/HAL-MICCAI/CuRIOUS@MICCAI.

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

B. Landman, H. Roth, Ali Hatamizadeh, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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

Ronald M. Summers, Jiamin Liu, Jianhua Yao, 2015, IEEE Transactions on Medical Imaging.

Anatomy-specific classification of medical images using deep convolutional nets

Ronald M. Summers, Jianhua Yao, Holger Roth, 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

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

S. Harmon, H. Roth, E. Turkbey, 2020, Medical Image Analysis.

Toward Foundational Deep Learning Models for Medical Imaging in the New Era of Transformer Networks.

H. Roth, M. Willemink, V. Sandfort, 2022, Radiology. Artificial intelligence.

Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning pdf

Daguang Xu, Masahiro Oda, Weichung Wang, 2020, DART/DCL@MICCAI.

Spatial aggregation of holistically‐nested convolutional neural networks for automated pancreas localization and segmentation☆

Ronald M. Summers, Andrew Sohn, Nathan Lay, 2017, Medical Image Anal..

NVIDIA FLARE: Federated Learning from Simulation to Real-World

Sean Bin Yang, Mona G. Flores, Ziyue Xu, 2022, IEEE Data Eng. Bull..

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

Daguang Xu, Baris Turkbey, Jesse Tetreault, 2021, J. Am. Medical Informatics Assoc..

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

Colin B. Compas, Richard D. White, Mona G. Flores, 2020, DART/DCL@MICCAI.

Multi-task Federated Learning for Heterogeneous Pancreas Segmentation pdf

Daguang Xu, Masahiro Oda, Kensaku Mori, 2021, CLIP/DCL/LL-COVID19/PPML@MICCAI.

Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures pdf

Daguang Xu, Andriy Myronenko, Wentao Zhu, 2021, MICCAI.

Fully automated prostate whole gland and central gland segmentation on MRI using holistically nested networks with short connections

Nathan Lay, Baris Turkbey, Ruida Cheng, 2019, Journal of medical imaging.

Towards Automated Colonoscopy Diagnosis: Binary Polyp Size Estimation via Unsupervised Depth Learning

Hayato Itoh, Holger Roth, Le Lu, 2018, MICCAI.

Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation pdf

Daguang Xu, Ling Zhang, Fausto Milletari, 2019, MICCAI.

Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications pdf

Ronald M. Summers, Jianhua Yao, Holger Roth, 2014, ArXiv.

MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images

S. Ourselin, Fernando Pérez-García, M. Cardoso, 2022, Medical Image Anal..

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

H. Roth, Ali Hatamizadeh, Daguang Xu, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Joint Multi Organ and Tumor Segmentation from Partial Labels Using Federated Learning

H. Roth, C. Fuh, M. Oda, 2022, DeCaF/FAIR@MICCAI.

DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images

S. Ourselin, S. Pieper, M. Cardoso, 2023, DALI@MICCAI.

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

Daguang Xu, Holger Roth, Can Zhao, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Going to Extremes: Weakly Supervised Medical Image Segmentation pdf

Dong Yang, Daguang Xu, Xiaosong Wang, 2020, Mach. Learn. Knowl. Extr..

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

Andriy Myronenko, Holger R. Roth, Ziyue Xu, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

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

Yuichiro Hayashi, Xiangrong Zhou, Holger Roth, 2018, Comput. Medical Imaging Graph..

On the influence of Dice loss function in multi-class organ segmentation of abdominal CT using 3D fully convolutional networks pdf

Yuichiro Hayashi, Holger Roth, Chen Shen, 2018, ArXiv.

When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation pdf

Dong Yang, Thomas Sanford, Daguang Xu, 2019, ArXiv.

UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation

H. Roth, Ali Hatamizadeh, Daguang Xu, 2022, ArXiv.

4D CNN for semantic segmentation of cardiac volumetric sequences

Dong Yang, Daguang Xu, Holger Roth, 2019, STACOM@MICCAI.

Automatic Lymph Node Cluster Segmentation Using Holistically-Nested Neural Networks and Structured Optimization in CT Images

Ronald M. Summers, Nathan Lay, Holger Roth, 2016, MICCAI.

Unsupervised Segmentation of Micro-CT Images of Lung Cancer Specimen Using Deep Generative Models

Hirohisa Oda, Kensaku Mori, Takayasu Moriya, 2019, MICCAI.

BESNet: Boundary-Enhanced Segmentation of Cells in Histopathological Images

Holger Roth, Hirohisa Oda, Masahiro Oda, 2018, MICCAI.

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

Daguang Xu, Baris Turkbey, Holger Roth, 2021, ArXiv.

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

Ronald M. Summers, Jiamin Liu, Holger Roth, 2015, IEEE Transactions on Image Processing.

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

Holger Roth, Andriy Myronenko, Ziyue Xu, 2021, MICCAI.

Fair Federated Medical Image Segmentation via Client Contribution Estimation

Ziyue Xu, H. Roth, Daguang Xu, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation

Ronald M. Summers, Andrew Sohn, Holger Roth, 2016, MICCAI.

Guest Editorial Special Issue on Federated Learning for Medical Imaging: Enabling Collaborative Development of Robust AI Models

Quanzheng Li, Shadi Albarqouni, H. Roth, 2023, IEEE Trans. Medical Imaging.

Do Gradient Inversion Attacks Make Federated Learning Unsafe?

Mona G. Flores, J. Kautz, H. Roth, 2022, IEEE Transactions on Medical Imaging.

Towards Understanding the Risks of Gradient Inversion in Federated Learning

Mona G. Flores, J. Kautz, H. Roth, 2021 .

Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI

P. Choyke, M. Merino, S. Harmon, 2022, Abdominal Radiology.

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

P. Choyke, M. Merino, S. Harmon, 2021, Academic radiology.

Registration of the endoluminal surfaces of the colon derived from prone and supine CT colonography.

Marc Modat, Steve Halligan, Sebastien Ourselin, 2011, Medical physics.

Hierarchical 3D fully convolutional networks for multi-organ segmentation pdf

Yuichiro Hayashi, Holger Roth, Hirohisa Oda, 2017, ArXiv.

Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks

Yuichiro Hayashi, Holger Roth, Hirohisa Oda, 2017, Medical Imaging.

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

S. Harmon, H. Roth, E. Turkbey, 2021, Medical Image Analysis.

2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers

Ronald M. Summers, Jiamin Liu, Holger Roth, 2014, MICCAI.

Colon Shape Estimation Method for Colonoscope Tracking Using Recurrent Neural Networks pdf

Nassir Navab, Holger Roth, Masahiro Oda, 2018, MICCAI.

Tracking and Segmentation of the Airways in Chest CT Using a Fully Convolutional Network

Holger Roth, Masahiro Oda, Takayuki Kitasaka, 2017, MICCAI.

Deep learning with orthogonal volumetric HED segmentation and 3D surface reconstruction model of prostate MRI

Ronald M. Summers, Nathan Lay, Baris Turkbey, 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

Cardiac Segmentation of LGE MRI with Noisy Labels

Daguang Xu, Wentao Zhu, Ziyue Xu, 2019, STACOM@MICCAI.

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

Dong Yang, Daguang Xu, Holger Roth, 2018, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

NeurReg: Neural Registration and Its Application to Image Segmentation

Daguang Xu, Wenqi Li, Wentao Zhu, 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

Automatic Segmentation of Rare Pediatric Brain Tumors Using Knowledge Transfer From Adult Data

M. Linguraru, H. Roth, R. Packer, 2023, 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI).

From adult to pediatric: deep learning-based automatic segmentation of rare pediatric brain tumors

M. Linguraru, H. Roth, Xinyang Liu, 2023, Medical Imaging.

Deep convolutional networks for automated detection of posterior-element fractures on spine CT

Ronald M. Summers, Jianhua Yao, Holger Roth, 2016, SPIE Medical Imaging.

Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks

Nathan Lay, Baris Turkbey, Le Lu, 2017, Journal of medical imaging.

Automatic MR prostate segmentation by deep learning with holistically-nested networks

Ronald M. Summers, Nathan Lay, Baris Turkbey, 2017, Medical Imaging.

Endoluminal surface registration for CT colonography using haustral fold matching☆

David J. Hawkes, Holger Roth, Thomas Hampshire, 2013, Medical Image Anal..

A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation pdf

Yuichiro Hayashi, Holger Roth, Chen Shen, 2018, MICCAI.

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

Dong Yang, Daguang Xu, Xiaosong Wang, 2020, ArXiv.

Efficient False Positive Reduction in Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation

Ronald M. Summers, Jiamin Liu, Jianhua Yao, 2017, Deep Learning and Convolutional Neural Networks for Medical Image Computing.

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

Daguang Xu, Holger Roth, Vishwesh Nath, 2021, MICCAI.

Active appearance model and deep learning for more accurate prostate segmentation on MRI

Ronald M. Summers, Peter L. Choyke, Matthew J. McAuliffe, 2016, SPIE Medical Imaging.

Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection

Ronald M. Summers, Adrian Barbu, Holger Roth, 2015, MICCAI.

Generalized chest CT and lab curves throughout the course of COVID-19

Mona G. Flores, S. Harmon, H. Roth, 2021, Scientific Reports.

Interactive 3D Segmentation Editing and Refinement via Gated Graph Neural Networks

Daguang Xu, Xiaosong Wang, Ling Zhang, 2019, GLMI@MICCAI.

Abdominal artery segmentation method from CT volumes using fully convolutional neural network

Masahiro Oda, Takayuki Kitasaka, Kensaku Mori, 2019, International Journal of Computer Assisted Radiology and Surgery.

Ensembled Prediction of Rheumatic Heart Disease from Ungated Doppler Echocardiography Acquired in Low-Resource Settings

M. Linguraru, H. Roth, E. Okello, 2022, MICCAI.

LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation

Ziyue Xu, H. Roth, Wenqi Li, 2020, MICCAI.

Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples

Ziyue Xu, H. Roth, Daguang Xu, 2023, 2023 IEEE/CVF International Conference on Computer Vision (ICCV).

ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated Data

H. Roth, C. Fuh, M. Oda, 2023, ISIC/Care-AI/MedAGI/DeCaF@MICCAI.

End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation pdf

Lin Yang, Daguang Xu, Yingda Xia, 2019, MLMI@MICCAI.

Deep learning and its application to medical image segmentation pdf

Yuichiro Hayashi, Holger Roth, Chen Shen, 2018, ArXiv.

Precise Estimation of Renal Vascular Dominant Regions Using Spatially Aware Fully Convolutional Networks, Tensor-Cut and Voronoi Diagrams

T. Kitasaka, K. Mori, H. Roth, 2019, Comput. Medical Imaging Graph..

Tunable CT Lung Nodule Synthesis Conditioned on Background Image and Semantic Features

Daguang Xu, Xiaosong Wang, Ling Zhang, 2019, SASHIMI@MICCAI.

Rapid artificial intelligence solutions in a pandemic—The COVID-19-20 Lung CT Lesion Segmentation Challenge

Mona G. Flores, V. Kovalev, V. Liauchuk, 2022, Medical Image Analysis.

UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation

H. Roth, Ali Hatamizadeh, Daguang Xu, 2022, arXiv.org.

Automatic Pancreas Segmentation Using Coarse-to-Fine Superpixel Labeling

Ronald M. Summers, Jiamin Liu, Holger Roth, 2017, Deep Learning and Convolutional Neural Networks for Medical Image Computing.

Convolutional neural network based deep-learning architecture for prostate cancer detection on multiparametric magnetic resonance images

Ronald M. Summers, Jin Tae Kwak, Baris Turkbey, 2017, Medical Imaging.

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

Daguang Xu, Dong Yang, Richard D. White, 2020, ArXiv.

Effective hyperparameter optimization with proxy data for multi-organ segmentation

K. Mori, H. Roth, M. Oda, 2022, Medical Imaging.