| End-to-end privacy preserving deep learning on multi-institutional medical imaging G Kaissis*, A Ziller*, J Passerat-Palmbach, T Ryffel, D Usynin, A Trask, ... Nature Machine Intelligence 3 (6), 473-484, 2021 | 257 | 2021 |
| Pysyft: A library for easy federated learning A Ziller, A Trask, A Lopardo, B Szymkow, B Wagner, E Bluemke, ... Federated learning systems: Towards next-generation AI, 111-139, 2021 | 162 | 2021 |
| Medical imaging deep learning with differential privacy A Ziller, D Usynin, R Braren, M Makowski, D Rueckert, G Kaissis Scientific Reports 11 (1), 13524, 2021 | 109 | 2021 |
| Adversarial interference and its mitigations in privacy-preserving collaborative machine learning D Usynin, A Ziller, M Makowski, R Braren, D Rueckert, B Glocker, ... Nature Machine Intelligence 3 (9), 749-758, 2021 | 41 | 2021 |
| Differentially private training of residual networks with scale normalisation H Klause, A Ziller, D Rueckert, K Hammernik, G Kaissis arXiv preprint arXiv:2203.00324, 2022 | 21 | 2022 |
| Differentially private federated deep learning for multi-site medical image segmentation A Ziller, D Usynin, N Remerscheid, M Knolle, M Makowski, R Braren, ... arXiv preprint arXiv:2107.02586, 2021 | 14 | 2021 |
| Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging S Tayebi Arasteh, A Ziller, C Kuhl, M Makowski, S Nebelung, R Braren, ... Communications Medicine 4 (1), 46, 2024 | 11* | 2024 |
| Privacy-preserving medical image analysis A Ziller, J Passerat-Palmbach, T Ryffel, D Usynin, A Trask, IDLC Junior, ... arXiv preprint arXiv:2012.06354, 2020 | 9 | 2020 |
| Complex-valued deep learning with differential privacy A Ziller, D Usynin, M Knolle, K Hammernik, D Rueckert, G Kaissis arXiv preprint arXiv:2110.03478, 2021 | 7 | 2021 |
| Oktoberfest food dataset A Ziller, J Hansjakob, V Rusinov, D Zügner, P Vogel, S Günnemann arXiv preprint arXiv:1912.05007, 2019 | 7 | 2019 |
| Smoothnets: Optimizing cnn architecture design for differentially private deep learning NW Remerscheid, A Ziller, D Rueckert, G Kaissis arXiv preprint arXiv:2205.04095, 2022 | 6 | 2022 |
| A unified interpretation of the gaussian mechanism for differential privacy through the sensitivity index G Kaissis, M Knolle, F Jungmann, A Ziller, D Usynin, D Rueckert arXiv preprint arXiv:2109.10528, 2021 | 6 | 2021 |
| Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy G Kaissis, J Hayes, A Ziller, D Rueckert arXiv preprint arXiv:2307.03928, 2023 | 5 | 2023 |
| Federated Learning Systems A Ziller, A Trask, A Lopardo, B Szymkow, B Wagner, E Bluemke, ... Cham: Springer, 111-139, 2021 | 5 | 2021 |
| Artificial intelligence in medicine and privacy preservation A Ziller, J Passerat-Palmbach, A Trask, R Braren, D Rueckert, G Kaissis Artificial Intelligence in Medicine, 1-14, 2020 | 5 | 2020 |
| Partial sensitivity analysis in differential privacy TT Mueller, A Ziller, D Usynin, M Knolle, F Jungmann, D Rueckert, ... arXiv preprint arXiv:2109.10582, 2021 | 3 | 2021 |
| Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation A Ziller, D Usynin, M Knolle, K Prakash, A Trask, R Braren, M Makowski, ... arXiv preprint arXiv:2107.04265, 2021 | 3 | 2021 |
| Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning G Kaissis, A Ziller, S Kolek, A Riess, D Rueckert Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
| Body fat estimation from surface meshes using graph neural networks TT Mueller, S Zhou, S Starck, F Jungmann, A Ziller, O Aksoy, D Movchan, ... International Workshop on Shape in Medical Imaging, 105-117, 2023 | 2 | 2023 |
| How Do Input Attributes Impact the Privacy Loss in Differential Privacy? TT Mueller, S Kolek, F Jungmann, A Ziller, D Usynin, M Knolle, ... arXiv preprint arXiv:2211.10173, 2022 | 2 | 2022 |
Georgios KaissisTechnical University of MunichVerified email at tum.de
