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A gentle introduction to deep learning in medical image processing

Zeitschrift für medizinische Physik, 2019-05, Vol.29 (2), p.86-101

2019

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Deep learning-based patient re-identification is able to exploit the biometric nature of medical chest X-ray data

Scientific reports, 2022-09, Vol.12 (1), p.14851-14851, Article 14851

2022

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Learning with known operators reduces maximum error bounds

Nature machine intelligence, 2019-08, Vol.1 (8), p.373-380

2019

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Technical Note: PYRO‐NN: Python reconstruction operators in neural networks

Medical physics (Lancaster), 2019-11, Vol.46 (11), p.5110-5115

2019

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The Artificial Neural Twin — Process optimization and continual learning in distributed process chains

Neural networks, 2024-12, Vol.180, p.106647, Article 106647

2024

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Multi-Modal Deep Guided Filtering for Comprehensible Medical Image Processing

IEEE transactions on medical imaging, 2020-05, Vol.39 (5), p.1703-1711

2020

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Known Operator Learning Enables Constrained Projection Geometry Conversion: Parallel to Cone-Beam for Hybrid MR/X-Ray Imaging

IEEE transactions on medical imaging, 2020-11, Vol.39 (11), p.3488-3498

2020

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Cephalogram synthesis and landmark detection in dental cone-beam CT systems

Medical image analysis, 2021-05, Vol.70, p.102028-102028, Article 102028

2021

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X-Ray Scatter Estimation Using Deep Splines

IEEE transactions on medical imaging, 2021-09, Vol.40 (9), p.2272-2283

2021

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Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging

Scientific reports, 2019-12, Vol.9 (1), p.18814-10, Article 18814

2019

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Precision Learning: Towards Use of Known Operators in Neural Networks

2018 24th International Conference on Pattern Recognition (ICPR), 2018, p.183-188

2018

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Detecting Anatomical Landmarks for Motion Estimation in Weight-Bearing Imaging of Knees

Machine Learning for Medical Image Reconstruction, p.83-90

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RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, p.92-100

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A computational framework for automated thrombus detection in NE-CT

Clinical neuroradiology (Munich), 2017-12, Vol.27 (S1), p.110

2017

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Learning with Known Operators reduces Maximum Training Error Bounds

Nature machine intelligence, 2019-08, Vol.1 (8), p.373-380

2019

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Known Operator Learning for a Hybrid Magnetic Resonance/X-Ray Imaging Acquisition Scheme

Syben-Leisner, Christopher

2021

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Known Operator Learning for a Hybrid Magnetic Resonance/X-Ray Imaging Acquisition Scheme

Syben-Leisner, Christopher

2021

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Deriving Neural Network Architectures Using Precision Learning: Parallel-to-Fan Beam Conversion

Pattern Recognition, p.503-517

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Simultaneous Estimation of X-Ray Back-Scatter and Forward-Scatter Using Multi-task Learning

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, p.199-208

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Guidance to Noise Simulation in X-ray Imaging

Bildverarbeitung für die Medizin 2024, 2024, p.184-189

2024

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Multi-Modal Super-Resolution with Deep Guided Filtering

Bildverarbeitung Für Die Medizin 2019, 2019, p.110-115

2019

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Abstract: Is Medical Chest X-ray Data Anonymous? Deep Learning-based Patient Re-identification is Able to Exploit the Biometric Nature of Medical Chest X-ray Data

Bildverarbeitung Für Die Medizin 2023, 2023, p.204-204

2023

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Age Estimation on Panoramic Dental X-ray Images using Deep Learning

Bildverarbeitung für die Medizin 2021, 2021, p.186-191

2021

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Self-Calibration and Simultaneous Motion Estimation for C-Arm CT Using Fiducial

Bildverarbeitung für die Medizin 2017, 2017, p.56-61

2017

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User Loss A Forced-Choice-Inspired Approach to Train Neural Networks Directly by User Interaction

Bildverarbeitung für die Medizin 2019, 2019, p.92-97

2019

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A gentle introduction to deep learning in medical image processing

Deep learning-based patient re-identification is able to exploit the biometric nature of medical chest X-ray data

Learning with known operators reduces maximum error bounds

Technical Note: PYRO‐NN: Python reconstruction operators in neural networks

The Artificial Neural Twin — Process optimization and continual learning in distributed process chains

Multi-Modal Deep Guided Filtering for Comprehensible Medical Image Processing

Known Operator Learning Enables Constrained Projection Geometry Conversion: Parallel to Cone-Beam for Hybrid MR/X-Ray Imaging

Cephalogram synthesis and landmark detection in dental cone-beam CT systems

X-Ray Scatter Estimation Using Deep Splines

Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging

Precision Learning: Towards Use of Known Operators in Neural Networks

Detecting Anatomical Landmarks for Motion Estimation in Weight-Bearing Imaging of Knees

RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting

A computational framework for automated thrombus detection in NE-CT

Learning with Known Operators reduces Maximum Training Error Bounds

Known Operator Learning for a Hybrid Magnetic Resonance/X-Ray Imaging Acquisition Scheme

Known Operator Learning for a Hybrid Magnetic Resonance/X-Ray Imaging Acquisition Scheme

Deriving Neural Network Architectures Using Precision Learning: Parallel-to-Fan Beam Conversion

Simultaneous Estimation of X-Ray Back-Scatter and Forward-Scatter Using Multi-task Learning

Guidance to Noise Simulation in X-ray Imaging

Multi-Modal Super-Resolution with Deep Guided Filtering

Abstract: Is Medical Chest X-ray Data Anonymous? Deep Learning-based Patient Re-identification is Able to Exploit the Biometric Nature of Medical Chest X-ray Data

Age Estimation on Panoramic Dental X-ray Images using Deep Learning

Self-Calibration and Simultaneous Motion Estimation for C-Arm CT Using Fiducial

User Loss A Forced-Choice-Inspired Approach to Train Neural Networks Directly by User Interaction

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