Daniël Maria Pelt

According to our database, Daniël Maria Pelt authored at least 19 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations.

Serban Vadineanu, Daniël Maria Pelt, Oleh Dzyubachyk, Kees Joost Batenburg

J. Imaging, July, 2024

Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction.

Jiayang Shi, Junyi Zhu, Daniël Maria Pelt, Kees Joost Batenburg, Matthew B. Blaschko

CoRR, 2024

2023

Multi-stage Deep Learning Artifact Reduction for Computed Tomography.

Jiayang Shi, Daniël Maria Pelt, Kees Joost Batenburg

CoRR, 2023

SR4ZCT: Self-supervised Through-Plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap.

Jiayang Shi, Daniël Maria Pelt, Kees Joost Batenburg

Proceedings of the Machine Learning in Medical Imaging - 14th International Workshop, 2023

2022

A tomographic workflow to enable deep learning for X-ray based foreign object detection.

Mathé T. Zeegers, Tristan van Leeuwen, Daniël Maria Pelt, Sophia Bethany Coban, Robert van Liere, Kees Joost Batenburg

Expert Syst. Appl., 2022

An Analysis of the Impact of Annotation Errors on the Accuracy of Deep Learning for Cell Segmentation.

Serban Vadineanu, Daniël Maria Pelt, Oleh Dzyubachyk, Kees Joost Batenburg

Proceedings of the International Conference on Medical Imaging with Deep Learning, 2022

2021

Improving reproducibility in synchrotron tomography using implementation-adapted filters.

Poulami Somanya Ganguly, Daniël Maria Pelt, Doga Gürsoy, Francesco De Carlo, Kees Joost Batenburg

CoRR, 2021

2020

Noise2Inverse: Self-Supervised Deep Convolutional Denoising for Tomography.

Allard A. Hendriksen, Daniël Maria Pelt, Kees Joost Batenburg

IEEE Trans. Computational Imaging, 2020

Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning.

Mathé T. Zeegers, Daniël Maria Pelt, Tristan van Leeuwen, Robert van Liere, Kees Joost Batenburg

J. Imaging, 2020

A Computationally Efficient Reconstruction Algorithm for Circular Cone-Beam Computed Tomography Using Shallow Neural Networks.

Marinus J. Lagerwerf, Daniël Maria Pelt, Willem Jan Palenstijn, Kees Joost Batenburg

J. Imaging, 2020

LEAN: graph-based pruning for convolutional neural networks by extracting longest chains.

Richard Schoonhoven, Allard A. Hendriksen, Daniël Maria Pelt, Kees Joost Batenburg

CoRR, 2020

Noise2Inverse: Self-supervised deep convolutional denoising for linear inverse problems in imaging.

Allard A. Hendriksen, Daniël Maria Pelt, Kees Joost Batenburg

CoRR, 2020

Real-time segmentation for tomographic imaging.

Richard Schoonhoven, Jan-Willem Buurlage, Daniël Maria Pelt, Kees Joost Batenburg

Proceedings of the 30th IEEE International Workshop on Machine Learning for Signal Processing, 2020

2018

Improving Tomographic Reconstruction from Limited Data Using Mixed-Scale Dense Convolutional Neural Networks.

Daniël Maria Pelt, Kees Joost Batenburg, James A. Sethian

J. Imaging, 2018

2016

A method for locally approximating regularized iterative tomographic reconstruction methods.

Daniël Maria Pelt, Kees Joost Batenburg

CoRR, 2016

2015

An exact algorithm for sparse matrix bipartitioning.

Daniël Maria Pelt, Rob H. Bisseling

J. Parallel Distributed Comput., 2015

2014

Improving Filtered Backprojection Reconstruction by Data-Dependent Filtering.

Daniël Maria Pelt, Kees Joost Batenburg

IEEE Trans. Image Process., 2014

A Medium-Grain Method for Fast 2D Bipartitioning of Sparse Matrices.

Daniël Maria Pelt, Rob H. Bisseling

Proceedings of the 2014 IEEE 28th International Parallel and Distributed Processing Symposium, 2014

2013

Fast Tomographic Reconstruction From Limited Data Using Artificial Neural Networks.

Daniël Maria Pelt, Kees Joost Batenburg

IEEE Trans. Image Process., 2013