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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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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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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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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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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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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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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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Anatomy-specific classification of medical images using deep convolutional nets

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015, p.101-104

2015

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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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A cascaded fully convolutional network framework for dilated pancreatic duct segmentation

International journal for computer assisted radiology and surgery, 2022-02, Vol.17 (2), p.343-354

2022

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Precise estimation of renal vascular dominant regions using spatially aware fully convolutional networks, tensor-cut and Voronoi diagrams

Computerized medical imaging and graphics, 2019-10, Vol.77, p.101642-101642, Article 101642

2019

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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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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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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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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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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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A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multi-organ Segmentation

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, p.417-425

Volltextzugriff (PDF)

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

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

2019

Volltextzugriff (PDF)

BESNet: Boundary-Enhanced Segmentation of Cells in Histopathological Images

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, p.228-236

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Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016, 2016, Vol.9901, p.451-459

2016

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

Journal of medical imaging (Bellingham, Wash.), 2020-03, Vol.7 (2), p.026001-026001

2020

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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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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

The future of digital health with federated learning

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

UNETR: Transformers for 3D Medical Image Segmentation

Anatomy-specific classification of medical images using deep convolutional nets

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

A cascaded fully convolutional network framework for dilated pancreatic duct segmentation

Precise estimation of renal vascular dominant regions using spatially aware fully convolutional networks, tensor-cut and Voronoi diagrams

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

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

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

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

Do Gradient Inversion Attacks Make Federated Learning Unsafe?

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

A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multi-organ Segmentation

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

BESNet: Boundary-Enhanced Segmentation of Cells in Histopathological Images

Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation

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

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

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

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