| 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 | 161 | 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 | 93 | 2021 |
| Medical imaging deep learning with differential privacy A Ziller, D Usynin, R Braren, M Makowski, D Rueckert, G Kaissis Scientific Reports 11 (1), 1-8, 2021 | 58 | 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 | 20 | 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 | 11 | 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 | 10 | 2021 |
| Privacy-preserving medical image analysis A Ziller, J Passerat-Palmbach, T Ryffel, D Usynin, A Trask, IDLC Junior, ... arXiv preprint arXiv:2012.06354, 2020 | 8 | 2020 |
| 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 | 5 | 2021 |
| 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 | 4 | 2021 |
| Federated Learning Systems A Ziller, A Trask, A Lopardo, B Szymkow, B Wagner, E Bluemke, ... Springer, 2021 | 4 | 2021 |
| 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 |
| Oktoberfest food dataset A Ziller, J Hansjakob, V Rusinov, D Zügner, P Vogel, S Günnemann arXiv preprint arXiv:1912.05007, 2019 | 3 | 2019 |
| Distributed Machine Learning and the Semblance of Trust D Usynin, A Ziller, D Rueckert, J Passerat-Palmbach, G Kaissis arXiv preprint arXiv:2112.11040, 2021 | 2 | 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 | 2 | 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 | 2 | 2020 |
| Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging ST Arasteh, A Ziller, C Kuhl, M Makowski, S Nebelung, R Braren, ... arXiv preprint arXiv:2302.01622, 2023 | 1 | 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 | 1 | 2022 |
| SmoothNets: Optimizing CNN architecture design for differentially private deep learning NW Remerscheid, A Ziller, D Rueckert, G Kaissis arXiv preprint arXiv:2205.04095, 2022 | 1 | 2022 |
| Privacy: An axiomatic approach A Ziller, TT Mueller, R Braren, D Rueckert, G Kaissis arXiv preprint arXiv:2203.11586, 2022 | 1 | 2022 |
| An automatic differentiation system for the age of differential privacy D Usynin, A Ziller, M Knolle, A Trask, K Prakash, D Rueckert, G Kaissis arXiv preprint arXiv:2109.10573, 2021 | 1 | 2021 |
Georgios KaissisTechnical University of MunichVerified email at tum.de
