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UNETR: Transformers for 3D Medical Image Segmentation

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, p.1748-1758

2022

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IEEE_Xplore

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

IEEE transactions on medical imaging, 2016-05, Vol.35 (5), p.1285-1298

2016

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IEEE_Xplore

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

IEEE transactions on medical imaging, 2016-05, Vol.35 (5), p.1170-1181

2016

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IEEE_Xplore

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

Medical image analysis, 2018-04, Vol.45, p.94-107

2018

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

IEEE transactions on image processing, 2017-01, Vol.26 (1), p.386-399

2017

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IEEE_Xplore

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

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, 2014, Vol.17 (Pt 1), p.520-527

2014

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

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, p.20698-20708

2022

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The future of digital health with federated learning

NPJ digital medicine, 2020-09, Vol.3 (1), p.119-119, Article 119

2020

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

Computerized medical imaging and graphics, 2018-06, Vol.66, p.90-99

2018

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

IEEE transactions on medical imaging, 2021-10, Vol.40 (10), p.2534-2547

2021

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IEEE_Xplore

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

IEEE transactions on medical imaging, 2020-07, Vol.39 (7), p.2531-2540

2020

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IEEE_Xplore

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

Medical image analysis, 2020-10, Vol.65, p.101766-101766, Article 101766

2020

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

Medical image analysis, 2021-05, Vol.70, p.101992-101992, Article 101992

2021

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Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health: Third MICCAI Workshop, DeCaF 2022, and Second MICCAI Workshop, FAIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, Proceedings

1, 2022

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

Journal of the American Medical Informatics Association : JAMIA, 2021-06, Vol.28 (6), p.1259-1264

2021

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Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience

Gastroenterology (New York, N.Y. 1943), 2018-06, Vol.154 (8), p.2027-2029.e3

2018

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Do Gradient Inversion Attacks Make Federated Learning Unsafe?

IEEE transactions on medical imaging, 2023-07, Vol.42 (7), p.1-1

2023

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IEEE_Xplore

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

Radiology, 2023-01, Vol.306 (1), p.172-182

2023

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

European radiology, 2019-03, Vol.29 (3), p.1074-1082

2019

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Performance improvement of weakly supervised fully convolutional networks by skip connections for brain structure segmentation

Medical physics (Lancaster), 2021-11, Vol.48 (11), p.7215-7227

2021

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

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, p.4125-4134

2020

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IEEE_Xplore

Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning: 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings

1, 2021

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

Radiology. Artificial intelligence, 2022-11, Vol.4 (6), p.e210284-e210284

2022

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

2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021, p.3942-3954

2021

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IEEE_Xplore

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

International journal for computer assisted radiology and surgery, 2019-12, Vol.14 (12), p.2069-2081

2019

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UNETR: Transformers for 3D Medical Image Segmentation

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

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

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

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

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

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

The future of digital health with federated learning

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

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

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

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

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

Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health: Third MICCAI Workshop, DeCaF 2022, and Second MICCAI Workshop, FAIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, Proceedings

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

Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience

Do Gradient Inversion Attacks Make Federated Learning Unsafe?

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

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

Performance improvement of weakly supervised fully convolutional networks by skip connections for brain structure segmentation

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

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

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

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

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