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Multi-stage Deep Learning Artifact Reduction for Computed Tomography
Jiayang Shi,
Daan Pelt,
Joost Batenburg,
Under review, 2023  
code
/
arXiv
A artifact reduction method for CT images based on deep learning. Three CNNs reduce artifacts in a
subsequent manner,
targeting different types of artifacts. This method is seamlessly integrated into existing CT
pipelines.
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SR4ZCT: Self-supervised Through-plane Resolution Enhancement for CT Images with
Arbitrary Resolution and Overlap
Jiayang Shi,
Daan Pelt,
Joost Batenburg,
Machine Learning in Medical Imaging (MLMI) in conjunction with MICCAI , 2023  
code
/
proceeding
/
paper
A self-supervised approach utilizing off-axis training to improve the resolution of through-plane CT scans.
This method is trained using in-plane images and applied to through-plane images, offering flexibility with
any resolution and overlap.
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LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques
Jiayang Shi,
Omar Elkilany,
Andreas Fischer,
Alexander Suppes,
Daan Pelt,
Joost Batenburg,
International Conference on Industrial Computed Tomography (iCT) , 2024  
code
/
dataset
/
proceeding
/
paper
A Benchmark Low-dose Industrial CT Dataset Tailored for Deep Learning: This work introduces a dataset specifically designed for industrial CT applications,
emphasizing deep learning approaches. It features a comprehensive analysis comparing the effectiveness and efficiency of 2D, 2.5D, and 3D training methodologies
in the context of denoising and image enhancement.
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Graph Neural Network based Log Anomaly Detection and Explanation
Zhong Li,
Jiayang Shi,
Matthijs van Leeuwen,
Under review , 2023  
code
/
arXiv
Graph neural network for log anomaly detection.
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Spectral reconstruction and disparity from spatio-spectrally coded light fields via
multi-task deep learning
Maximilian Schambach,
Jiayang Shi,
Michael Heizmann,
3DV , 2021  
code
/
proceeding
/
arXiv
A novel method to reconstruct a spectral central view and its aligned disparity map from spatio-spectrally
coded light fields using multi-task deep learning.
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