| Tumor-derived prostaglandin E2 programs cDC1 dysfunction to impair intratumoral orchestration of anti-cancer T cell responses F Bayerl, P Meiser, S Donakonda, A Hirschberger, SB Lacher, AM Pedde, ... Immunity 56 (6), 1341-1358. e11, 2023 | 53 | 2023 |
| A distinct stimulatory cDC1 subpopulation amplifies CD8+ T cell responses in tumors for protective anti-cancer immunity P Meiser, MA Knolle, A Hirschberger, GP de Almeida, F Bayerl, S Lacher, ... Cancer Cell 41 (8), 1498-1515. e10, 2023 | 35 | 2023 |
| 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 |
| Efficient, high-performance semantic segmentation using multi-scale feature extraction M Knolle, G Kaissis, F Jungmann, S Ziegelmayer, D Sasse, M Makowski, ... Plos one 16 (8), e0255397, 2021 | 12 | 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 | 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 Journal of Privacy and Confidentiality, 2021 | 7 | 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 | 4 | 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 |
| 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 |
| Bias-Aware Minimisation: Understanding and Mitigating Estimator Bias in Private SGD M Knolle, R Dorfman, A Ziller, D Rueckert, G Kaissis arXiv preprint arXiv:2308.12018, 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 |
| Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty M Knolle, A Ziller, D Usynin, R Braren, MR Makowski, D Rueckert, ... arXiv preprint arXiv:2107.04296, 2021 | 2 | 2021 |
| (Predictable) performance bias in unsupervised anomaly detection F Meissen, S Breuer, M Knolle, A Buyx, R Müller, G Kaissis, B Wiestler, ... EBioMedicine 101, 2024 | 1 | 2024 |
| Visual Privacy Auditing with Diffusion Models K Schwethelm, J Kaiser, M Knolle, D Rueckert, G Kaissis, A Ziller arXiv preprint arXiv:2403.07588, 2024 | | 2024 |
| SoK: Memorisation in machine learning D Usynin, M Knolle, G Kaissis arXiv preprint arXiv:2311.03075, 2023 | | 2023 |
| AI and Big Data in Cardiology: A Practical Guide (Diagnosis Chapter) D Rueckert, M Knolle, N Duchateau, R Razavi, G Kaissis Springer Nature, 2023 | | 2023 |
Daniel RueckertTechnical University of Munich and Imperial College London在 tum.de 的电子邮件经过验证
