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ORCIDDaniël Maria Pelt
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2020 – today
- 2024
[j9]Serban Vadineanu
, Daniël Maria Pelt, Oleh Dzyubachyk, Kees Joost Batenburg:
Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations. J. Imaging 10(7): 172 (2024)- [i9]Jiayang Shi, Junyi Zhu, Daniël Maria Pelt, Kees Joost Batenburg, Matthew B. Blaschko:
Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction. CoRR abs/2405.02509 (2024) - [i8]Jiayang Shi, Daniël Maria Pelt, Kees Joost Batenburg:
SR4ZCT: Self-supervised Through-plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap. CoRR abs/2405.02515 (2024) - 2023
- [c5]Serban Vadineanu, Daniël Maria Pelt, Oleh Dzyubachyk, Kees Joost Batenburg:
Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations. MILLanD@MICCAI 2023: 3-13 - [c4]Jiayang Shi, Daniël Maria Pelt, Kees Joost Batenburg:
SR4ZCT: Self-supervised Through-Plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap. MLMI@MICCAI (1) 2023: 52-61 - [i7]Jiayang Shi, Daniël Maria Pelt, Kees Joost Batenburg:
Multi-stage Deep Learning Artifact Reduction for Computed Tomography. CoRR abs/2309.00494 (2023) - 2022
- [j8]Mathé T. Zeegers, Tristan van Leeuwen, Daniël Maria Pelt, Sophia Bethany Coban, Robert van Liere, Kees Joost Batenburg:
A tomographic workflow to enable deep learning for X-ray based foreign object detection. Expert Syst. Appl. 206: 117768 (2022) - [c3]Serban Vadineanu, Daniël Maria Pelt, Oleh Dzyubachyk, Kees Joost Batenburg:
An Analysis of the Impact of Annotation Errors on the Accuracy of Deep Learning for Cell Segmentation. MIDL 2022: 1251-1267 - [i6]Mathé T. Zeegers, Tristan van Leeuwen, Daniël Maria Pelt, Sophia Bethany Coban, Robert van Liere, Kees Joost Batenburg:
A tomographic workflow to enable deep learning for X-ray based foreign object detection. CoRR abs/2201.12184 (2022) - 2021
- [i5]Poulami Somanya Ganguly, Daniël Maria Pelt, Doga Gürsoy, Francesco De Carlo, Kees Joost Batenburg:
Improving reproducibility in synchrotron tomography using implementation-adapted filters. CoRR abs/2103.08288 (2021) - 2020
- [j7]Mathé T. Zeegers, Daniël Maria Pelt, Tristan van Leeuwen, Robert van Liere, Kees Joost Batenburg:
Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning. J. Imaging 6(12): 132 (2020) - [j6]Marinus J. Lagerwerf, Daniël Maria Pelt, Willem Jan Palenstijn, Kees Joost Batenburg:
A Computationally Efficient Reconstruction Algorithm for Circular Cone-Beam Computed Tomography Using Shallow Neural Networks. J. Imaging 6(12): 135 (2020) - [j5]Allard A. Hendriksen, Daniël Maria Pelt, Kees Joost Batenburg:
Noise2Inverse: Self-Supervised Deep Convolutional Denoising for Tomography. IEEE Trans. Computational Imaging 6: 1320-1335 (2020) - [c2]Richard Schoonhoven, Jan-Willem Buurlage, Daniël Maria Pelt, Kees Joost Batenburg:
Real-time segmentation for tomographic imaging. MLSP 2020: 1-6 - [i4]Allard A. Hendriksen, Daniël Maria Pelt, Kees Joost Batenburg:
Noise2Inverse: Self-supervised deep convolutional denoising for linear inverse problems in imaging. CoRR abs/2001.11801 (2020) - [i3]Marinus J. Lagerwerf, Daniël Maria Pelt, Willem Jan Palenstijn, Kees Joost Batenburg:
A computationally efficient reconstruction algorithm for circular cone-beam computed tomography using shallow neural networks. CoRR abs/2010.00421 (2020) - [i2]Richard Schoonhoven, Allard A. Hendriksen, Daniël Maria Pelt, Kees Joost Batenburg:
LEAN: graph-based pruning for convolutional neural networks by extracting longest chains. CoRR abs/2011.06923 (2020)
2010 – 2019
- 2018
- [j4]Daniël Maria Pelt, Kees Joost Batenburg, James A. Sethian:
Improving Tomographic Reconstruction from Limited Data Using Mixed-Scale Dense Convolutional Neural Networks. J. Imaging 4(11): 128 (2018) - 2016
- [i1]Daniël Maria Pelt, Kees Joost Batenburg:
A method for locally approximating regularized iterative tomographic reconstruction methods. CoRR abs/1604.02292 (2016) - 2015
- [j3]Daniël Maria Pelt, Rob H. Bisseling:
An exact algorithm for sparse matrix bipartitioning. J. Parallel Distributed Comput. 85: 79-90 (2015) - 2014
- [j2]Daniël Maria Pelt, Kees Joost Batenburg:
Improving Filtered Backprojection Reconstruction by Data-Dependent Filtering. IEEE Trans. Image Process. 23(11): 4750-4762 (2014) - [c1]Daniël Maria Pelt, Rob H. Bisseling:
A Medium-Grain Method for Fast 2D Bipartitioning of Sparse Matrices. IPDPS 2014: 529-539 - 2013
- [j1]Daniël Maria Pelt, Kees Joost Batenburg:
Fast Tomographic Reconstruction From Limited Data Using Artificial Neural Networks. IEEE Trans. Image Process. 22(12): 5238-5251 (2013)
Coauthor Index
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