| Noise2inverse: Self-supervised deep convolutional denoising for tomography AA Hendriksen, DM Pelt, KJ Batenburg IEEE Transactions on Computational Imaging 6, 1320-1335, 2020 | 150 | 2020 |
| Segmentation of dental cone‐beam CT scans affected by metal artifacts using a mixed‐scale dense convolutional neural network J Minnema, M van Eijnatten, AA Hendriksen, N Liberton, DM Pelt, ... Medical physics 46 (11), 5027-5035, 2019 | 78 | 2019 |
| Optional stopping with Bayes factors: a categorization and extension of folklore results, with an application to invariant situations A Hendriksen, R de Heide, P Grünwald Bayesian Analysis 16 (3), 961-989, 2021 | 43 | 2021 |
| Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data AA Hendriksen, M Bührer, L Leone, M Merlini, N Vigano, DM Pelt, ... Scientific reports 11 (1), 11895, 2021 | 37 | 2021 |
| Tomosipo: fast, flexible, and convenient 3D tomography for complex scanning geometries in Python AA Hendriksen, D Schut, WJ Palenstijn, N Viganó, J Kim, DM Pelt, ... Optics Express 29 (24), 40494-40513, 2021 | 24 | 2021 |
| Prototyping X-ray tomographic reconstruction pipelines with FleXbox A Kostenko, WJ Palenstijn, SB Coban, AA Hendriksen, R van Liere, ... SoftwareX 11, 100364, 2020 | 17 | 2020 |
| On-the-fly machine learning for improving image resolution in tomography AA Hendriksen, DM Pelt, WJ Palenstijn, SB Coban, KJ Batenburg Applied Sciences 9 (12), 2445, 2019 | 16 | 2019 |
| Foam-like phantoms for comparing tomography algorithms DM Pelt, AA Hendriksen, KJ Batenburg Journal of Synchrotron Radiation 29 (1), 254-265, 2022 | 8 | 2022 |
| Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D computed tomography MJ Lagerwerf, AA Hendriksen, JW Buurlage, KJ Batenburg Machine Learning: Science and Technology 2 (1), 015012, 2020 | 8 | 2020 |
| ahendriksenh/msd_pytorch: v0. 7.2 AA Hendriksen Version v0 7 (10.5281), 2019 | 7 | 2019 |
| Deep learning based classification of dynamic processes in time-resolved X-ray tomographic microscopy M Bührer, H Xu, AA Hendriksen, FN Büchi, J Eller, M Stampanoni, ... Scientific Reports 11 (1), 24174, 2021 | 5 | 2021 |
| Rianne de Heide, and Peter Grünwald. Optional stopping with bayes factors: a categorization and extension of folklore results, with an application to invariant situations A Hendriksen Bayesian Analysis 16 (3), 961-989, 2021 | 5 | 2021 |
| Betting as an alternative to p-values AA Hendriksen Master’s thesis, 2017 | 5 | 2017 |
| LEAN: graph-based pruning for convolutional neural networks by extracting longest chains R Schoonhoven, AA Hendriksen, DM Pelt, KJ Batenburg arXiv preprint arXiv:2011.06923, 2020 | 4 | 2020 |
| Deep‐learning‐based joint rigid and deformable contour propagation for magnetic resonance imaging‐guided prostate radiotherapy ID Kolenbrander, M Maspero, AA Hendriksen, R Pollitt, ... Medical Physics 51 (4), 2367-2377, 2024 | 3 | 2024 |
| How auto-differentiation can improve CT workflows: classical algorithms in a modern framework R Schoonhoven, A Skorikov, WJ Palenstijn, DM Pelt, AA Hendriksen, ... Optics Express 32 (6), 9019-9041, 2024 | 1 | 2024 |
| Deep learning for tomographic reconstruction with limited data AA Hendriksen PhD thesis. Leiden University, 2022 (cit. on p. 35), 2022 | 1 | 2022 |
| CT image segmentation for additive manufactured skull implants using deep learning J Minnema, M van Eijnatten, J Wolff, AA Hendriksen, KJ Batenburg, ... Transactions on Additive Manufacturing Meets Medicine 1 (1), 2019 | | 2019 |
| Percolatietheorie op bomen AA Hendriksen | | |
