| 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 | 287 | 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 | 183 | 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 | 122 | 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 | 45 | 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 | 22 | 2022 |
| 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 | 20* | 2024 |
| 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 | 16 | 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 | 10 | 2020 |
| Oktoberfest food dataset A Ziller, J Hansjakob, V Rusinov, D Zügner, P Vogel, S Günnemann arXiv preprint arXiv:1912.05007, 2019 | 8 | 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 | 7 | 2022 |
| 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 |
| 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 | 7 | 2021 |
| End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nat. Mach. Intell. 3 G Kaissis, A Ziller, J Passerat-Palmbach, T Ryffel, D Usynin, A Trask, ... | 7 | 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 |
| 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 | 4 | 2021 |
| Prognostic value of deep learning-derived body composition in advanced pancreatic cancer—a retrospective multicenter study J Keyl, A Bucher, F Jungmann, R Hosch, A Ziller, R Armbruster, ... ESMO open 9 (1), 102219, 2024 | 3 | 2024 |
| 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 |
| 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 | 3 | 2021 |
Georgios KaissisTechnical University of MunichVerified email at tum.de
