Georgios Kaissis

160 papers A* 6A 1B 1Journal 115Unranked 35
YearRankTypeTitle / Venue / Authors
2026 J jnl
An Exploratory Survey Study Toward a Conceptual Framework for Structuring the Integration of Privacy-Enhancing Technologies in Federated Learning and Analytics.
IEEE Access
Hammam Abu Attieh, Mehmed Halilovic, Raouf Kerkouche, Shadi Rahimian, Hui-Po Wang, Raphael Rehms, Georgios Kaissis, Alexander Ziller, Jonas Kuntzer, Karen Otte, Thierry Meurers, Mario Fritz, Daniel Rückert, Fabian Prasser
2026 J jnl
Optimal conversion from Rényi Differential Privacy to
CoRR
Anneliese Riess, Juan Felipe Gómez, Flávio du Pin Calmon, Julia Anne Schnabel, Georgios Kaissis
2026 J jnl
Step-resolved data attribution for looped transformers.
CoRR
Georgios Kaissis, David Mildenberger, Juan Felipe Gómez, Martin J. Menten, Eleni Triantafillou
2026 — conf
Unintended Memorization of Sensitive Information in Fine-Tuned Language Models.
EACL (Volume 1: Long Papers)
Marton Szep, Jorge Marin Ruiz, Georgios Kaissis, Paulina Seidl, Rüdiger von Eisenhart-Rothe, Florian Hinterwimmer, Daniel Rueckert
2026 J jnl
Unintended Memorization of Sensitive Information in Fine-Tuned Language Models.
CoRR
Marton Szep, Jorge Marin Ruiz, Georgios Kaissis, Paulina Seidl, Rüdiger von Eisenhart-Rothe, Florian Hinterwimmer, Daniel Rueckert
2026 J jnl
Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacy.
CoRR
Johannes Kaiser, Alexander Ziller, Eleni Triantafillou, Daniel Rückert, Georgios Kaissis
2025 J jnl
(ϵ, δ) Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees.
CoRR
Juan Felipe Gómez, Bogdan Kulynych, Georgios Kaissis, Jamie Hayes, Borja Balle, Antti Honkela
2025 J jnl
Beyond Benchmarks: Dynamic, Automatic And Systematic Red-Teaming Agents For Trustworthy Medical Language Models.
CoRR
Jiazhen Pan, Bailiang Jian, Paul Hager, Yundi Zhang, Che Liu, Friederike Jungmann, Hongwei Bran Li, Chenyu You, Junde Wu, Jiayuan Zhu, Fenglin Liu, Yuyuan Liu, Niklas Bubeck, Christian Wachinger, Chen Chen, Zhenyu Gong, Cheng Ouyang, Georgios Kaissis, Benedikt Wiestler, Daniel Rueckert
2025 J jnl
Counterfactual Influence as a Distributional Quantity.
CoRR
Matthieu Meeus, Igor Shilov, Georgios Kaissis, Yves-Alexandre de Montjoye
2025 A conf
Cross-Domain and Cross-Dimension Learning for Image-to-Graph Transformers.
WACV
Alexander H. Berger, Laurin Lux, Suprosanna Shit, Ivan Ezhov, Georgios Kaissis, Martin J. Menten, Daniel Rueckert, Johannes C. Paetzold
2025 — conf
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy.
SaTML
Kristian Schwethelm, Johannes Kaiser, Jonas Kuntzer, Mehmet Yigitsoy, Daniel Rückert, Georgios Kaissis
2025 J jnl
Efficient numeracy in language models through single-token number embeddings.
CoRR
Linus Kreitner, Paul Hager, Jonathan Mengedoht, Georgios Kaissis, Daniel Rueckert, Martin J. Menten
2025 J jnl
Gradient-Weight Alignment as a Train-Time Proxy for Generalization in Classification Tasks.
CoRR
Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis
2025 J jnl
Improved Localized Machine Unlearning Through the Lens of Memorization.
Trans. Mach. Learn. Res.
Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Georgios Kaissis, Daniel Rueckert, Gintare Karolina Dziugaite, Eleni Triantafillou
2025 A* conf
Laplace Sample Information: Data Informativeness Through a Bayesian Lens.
ICLR
Johannes Kaiser, Kristian Schwethelm, Daniel Rueckert, Georgios Kaissis
2025 J jnl
Laplace Sample Information: Data Informativeness Through a Bayesian Lens.
CoRR
Johannes Kaiser, Kristian Schwethelm, Daniel Rueckert, Georgios Kaissis
2025 J jnl
LungEvaty: A Scalable, Open-Source Transformer-based Deep Learning Model for Lung Cancer Risk Prediction in LDCT Screening.
CoRR
Johannes Brandt, Maulik Chevli, Rickmer Braren, Georgios Kaissis, Philip Müller, Daniel Rueckert
2025 — conf
Memorisation Bias: AI Predictions for Data Contributors Are Biased Towards Their Health States in the Training Data.
LMID@MICCAI
Moritz Knolle, Martin J. Menten, Daniel Rueckert, Georgios Kaissis, Ben Glocker
2025 J jnl
On Arbitrary Predictions from Equally Valid Models.
CoRR
Sarah Lockfisch, Kristian Schwethelm, Martin J. Menten, Rickmer Braren, Daniel Rueckert, Alexander Ziller, Georgios Kaissis
2025 J jnl
Redirection for Erasing Memory (REM): Towards a universal unlearning method for corrupted data.
CoRR
Stefan Schoepf, Michael Curtis Mozer, Nicole Mitchell, Alexandra Brintrup, Georgios Kaissis, Peter Kairouz, Eleni Triantafillou
2025 A* conf
Scaling Laws for Differentially Private Language Models.
ICML
Ryan McKenna, Yangsibo Huang, Amer Sinha, Borja Balle, Zachary Charles, Christopher A. Choquette-Choo, Badih Ghazi, Georgios Kaissis, Ravi Kumar, Ruibo Liu, Da Yu, Chiyuan Zhang
2025 J jnl
Scaling Laws for Differentially Private Language Models.
CoRR
Ryan McKenna, Yangsibo Huang, Amer Sinha, Borja Balle, Zachary Charles, Christopher A. Choquette-Choo, Badih Ghazi, Georgios Kaissis, Ravi Kumar, Ruibo Liu, Da Yu, Chiyuan Zhang
2025 J jnl
Strong Membership Inference Attacks on Massive Datasets and (Moderately) Large Language Models.
CoRR
Jamie Hayes, Ilia Shumailov, Christopher A. Choquette-Choo, Matthew Jagielski, Georgios Kaissis, Katherine Lee, Milad Nasr, Sahra Ghalebikesabi, Niloofar Mireshghallah, Meenatchi Sundaram Mutu Selva Annamalai, Igor Shilov, Matthieu Meeus, Yves-Alexandre de Montjoye, Franziska Boenisch, Adam Dziedzic, A. Feder Cooper
2025 J jnl
The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing.
CoRR
Meenatchi Sundaram Muthu Selva Annamalai, Borja Balle, Jamie Hayes, Georgios Kaissis, Emiliano De Cristofaro
2025 J jnl
Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy.
CoRR
Bogdan Kulynych, Juan Felipe Gómez, Georgios Kaissis, Jamie Hayes, Borja Balle, Flávio P. Calmon, Jean Louis Raisaro
2025 J jnl
VaultGemma: A Differentially Private Gemma Model.
CoRR
Amer Sinha, Thomas Mesnard, Ryan McKenna, Daogao Liu, Christopher A. Choquette-Choo, Yangsibo Huang, Da Yu, Georgios Kaissis, Zachary Charles, Ruibo Liu, Lynn Chua, Pritish Kamath, Pasin Manurangsi, Steve He, Chiyuan Zhang, Badih Ghazi, Borja de Balle Pigem, Prem Eruvbetine, Tris Warkentin, Armand Joulin, Ravi Kumar
2025 J jnl
Weakly Supervised Object Detection in Chest X-Rays With Differentiable ROI Proposal Networks and Soft ROI Pooling.
IEEE Trans. Medical Imaging
Philip Müller, Felix Meissen, Georgios Kaissis, Daniel Rueckert
2024 J jnl
A Survey on Graph Construction for Geometric Deep Learning in Medicine: Methods and Recommendations.
Trans. Mach. Learn. Res.
Tamara T. Mueller, Sophie Starck, Alina Dima, Stephan Wunderlich, Kyriaki-Margarita Bintsi, Kamilia Zaripova, Rickmer Braren, Daniel Rueckert, Anees Kazi, Georgios Kaissis
2024 J jnl
Are Population Graphs Really as Powerful as Believed?
Trans. Mach. Learn. Res.
Tamara T. Müller, Sophie Starck, Kyriaki-Margarita Bintsi, Alexander Ziller, Rickmer Braren, Georgios Kaissis, Daniel Rueckert
2024 A* conf
Attack-Aware Noise Calibration for Differential Privacy.
NeurIPS
Bogdan Kulynych, Juan Felipe Gómez, Georgios Kaissis, Flávio P. Calmon, Carmela Troncoso
2024 J jnl
Attack-Aware Noise Calibration for Differential Privacy.
CoRR
Bogdan Kulynych, Juan Felipe Gómez, Georgios Kaissis, Flávio du Pin Calmon, Carmela Troncoso
2024 A* conf
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy.
ICML
Georgios Kaissis, Stefan Kolek, Borja Balle, Jamie Hayes, Daniel Rueckert
2024 J jnl
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy.
CoRR
Georgios Kaissis, Stefan Kolek, Borja Balle, Jamie Hayes, Daniel Rueckert
2024 J jnl
Bounding Reconstruction Attack Success of Adversaries Without Data Priors.
CoRR
Alexander Ziller, Anneliese Riess, Kristian Schwethelm, Tamara T. Mueller, Daniel Rueckert, Georgios Kaissis
2024 — conf
ChEX: Interactive Localization and Region Description in Chest X-Rays.
ECCV (21)
Philip Müller, Georgios Kaissis, Daniel Rueckert
2024 J jnl
ChEX: Interactive Localization and Region Description in Chest X-rays.
CoRR
Philip Müller, Georgios Kaissis, Daniel Rueckert
2024 — conf
Complex-Valued Federated Learning with Differential Privacy and MRI Applications.
ISIC/iMIMIC/EARTH/DeCaF@MICCAI
Anneliese Riess, Alexander Ziller, Stefan Kolek, Daniel Rueckert, Julia A. Schnabel, Georgios Kaissis
2024 J jnl
Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers.
CoRR
Alexander H. Berger, Laurin Lux, Suprosanna Shit, Ivan Ezhov, Georgios Kaissis, Martin J. Menten, Daniel Rueckert, Johannes C. Paetzold
2024 J jnl
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy.
CoRR
Kristian Schwethelm, Johannes Kaiser, Jonas Kuntzer, Mehmet Yigitsoy, Daniel Rueckert, Georgios Kaissis
2024 — conf
Differentially Private Graph Neural Networks for Medical Population Graphs and The Impact of The Graph Structure.
ISBI
Tamara T. Mueller, Maulik Chevli, Ameya Daigavane, Daniel Rueckert, Georgios Kaissis
2024 J jnl
Differentially Private Guarantees for Analytics and Machine Learning on Graphs: A Survey of Results.
J. Priv. Confidentiality
Tamara T. Mueller, Dmitrii Usynin, Johannes C. Paetzold, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
2024 J jnl
Encrypted federated learning for secure decentralized collaboration in cancer image analysis.
Medical Image Anal.
Daniel Truhn, Soroosh Tayebi Arasteh, Oliver Lester Saldanha, Gustav Müller-Franzes, Firas Khader, Philip Quirke, Nicholas P. West, Richard Gray, Gordon G. A. Hutchins, Jacqueline A. James, Maurice B. Loughrey, Manuel Salto-Tellez, Hermann Brenner, Alexander Brobeil, Tanwei Yuan, Jenny Chang-Claude, Michael Hoffmeister, Sebastian Foersch, Tianyu Han, Sebastian Keil, Maximilian Schulze-Hagen, Peter Isfort, Philipp Bruners, Georgios Kaissis, Christiane Kuhl, Sven Nebelung, Jakob Nikolas Kather
2024 — conf
Enhancing the Utility of Privacy-Preserving Cancer Classification Using Synthetic Data.
Deep-Breath@MICCAI
Richard Osuala, Daniel Lang, Anneliese Riess, Georgios Kaissis, Zuzanna Szafranowska, Grzegorz Skorupko, Oliver Díaz, Julia A. Schnabel, Karim Lekadir
2024 J jnl
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data.
CoRR
Richard Osuala, Daniel M. Lang, Anneliese Riess, Georgios Kaissis, Zuzanna Szafranowska, Grzegorz Skorupko, Oliver Díaz, Julia A. Schnabel, Karim Lekadir
2024 — conf
Fair and Private CT Contrast Agent Detection.
FAIMI/EPIMI@MICCAI
Philipp Kaess, Alexander Ziller, Lea Mantz, Daniel Rueckert, Florian J. Fintelmann, Georgios Kaissis
2024 J jnl
Federated learning is not a cure-all for data ethics.
Nat. Mac. Intell.
Marieke A. R. Bak, Vince I. Madai, Leo Anthony Celi, Georgios Kaissis, Ronald Cornet, Menno Maris, Daniel Rueckert, Alena Buyx, Stuart McLennan
2024 J jnl
Improved Localized Machine Unlearning Through the Lens of Memorization.
CoRR
Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Georgios Kaissis, Daniel Rueckert, Gintare Karolina Dziugaite, Eleni Triantafillou
2024 — conf
Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training.
EICC
Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
2024 J jnl
Kernel Normalized Convolutional Networks.
Trans. Mach. Learn. Res.
Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Daniel Rueckert, Georgios Kaissis
2024 J jnl
Machine Unlearning for Medical Imaging.
CoRR
Reza Nasirigerdeh, Nader Razmi, Julia A. Schnabel, Daniel Rueckert, Georgios Kaissis
2024 J jnl
Memorisation in Machine Learning: A Survey of Results.
Trans. Mach. Learn. Res.
Dmitrii Usynin, Moritz Knolle, Georgios Kaissis
2024 — conf
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models.
DGM4MICCAI@MICCAI
Deniz Daum, Richard Osuala, Anneliese Riess, Georgios Kaissis, Julia A. Schnabel, Maxime Di Folco
2024 J jnl
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models.
CoRR
Deniz Daum, Richard Osuala, Anneliese Riess, Georgios Kaissis, Julia A. Schnabel, Maxime Di Folco
2024 J jnl
Reconciling privacy and accuracy in AI for medical imaging.
Nat. Mac. Intell.
Alexander Ziller, Tamara T. Mueller, Simon Stieger, Leonhard F. Feiner, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
2024 J jnl
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art.
IEEE Trans. Medical Imaging
Ioannis Lagogiannis, Felix Meissen, Georgios Kaissis, Daniel Rueckert
2024 J jnl
Visual Privacy Auditing with Diffusion Models.
CoRR
Kristian Schwethelm, Johannes Kaiser, Moritz Knolle, Daniel Rueckert, Georgios Kaissis, Alexander Ziller
2024 J jnl
Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling.
CoRR
Philip Müller, Felix Meissen, Georgios Kaissis, Daniel Rueckert
2023 J jnl
(Predictable) Performance Bias in Unsupervised Anomaly Detection.
CoRR
Felix Meissen, Svenja Breuer, Moritz Knolle, Alena Buyx, Ruth Müller, Georgios Kaissis, Benedikt Wiestler, Daniel Rueckert
2023 — conf
Anatomy-Driven Pathology Detection on Chest X-rays.
MICCAI (1)
Philip Müller, Felix Meissen, Johannes Brandt, Georgios Kaissis, Daniel Rueckert
2023 J jnl
Anatomy-Driven Pathology Detection on Chest X-rays.
CoRR
Philip Müller, Felix Meissen, Johannes Brandt, Georgios Kaissis, Daniel Rueckert
2023 J jnl
Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks.
ACM Trans. Priv. Secur.
Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
2023 J jnl
Bias-Aware Minimisation: Understanding and Mitigating Estimator Bias in Private SGD.
CoRR
Moritz Knolle, Robert Dorfman, Alexander Ziller, Daniel Rueckert, Georgios Kaissis
2023 — conf
Body Fat Estimation from Surface Meshes Using Graph Neural Networks.
ShapeMI@MICCAI
Tamara T. Mueller, Siyu Zhou, Sophie Starck, Friederike Jungmann, Alexander Ziller, Orhun Aksoy, Danylo Movchan, Rickmer Braren, Georgios Kaissis, Daniel Rueckert
2023 J jnl
Body Fat Estimation from Surface Meshes using Graph Neural Networks.
CoRR
Tamara T. Mueller, Siyu Zhou, Sophie Starck, Friederike Jungmann, Alexander Ziller, Orhun Aksoy, Danylo Movchan, Rickmer Braren, Georgios Kaissis, Daniel Rueckert
2023 J jnl
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy.
CoRR
Georgios Kaissis, Jamie Hayes, Alexander Ziller, Daniel Rueckert
2023 — ch.
Diagnosis.
AI and Big Data in Cardiology
Daniel Rueckert, Moritz Knolle, Nicolas Duchateau, Reza Razavi, Georgios Kaissis
2023 J jnl
Differentially Private Graph Neural Networks for Whole-Graph Classification.
IEEE Trans. Pattern Anal. Mach. Intell.
Tamara T. Mueller, Johannes C. Paetzold, Chinmay Prabhakar, Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
2023 J jnl
Equivariant Differentially Private Deep Learning.
CoRR
Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis
2023 — conf
Equivariant Differentially Private Deep Learning: Why DP-SGD Needs Sparser Models.
AISec@CCS
Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis
2023 J jnl
Explainable 2D Vision Models for 3D Medical Data.
CoRR
Alexander Ziller, Alp Güvenir, Ayhan Can Erdur, Tamara T. Mueller, Philip Müller, Friederike Jungmann, Johannes Brandt, Jan Peeken, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
2023 — conf
Exploiting Segmentation Labels and Representation Learning to Forecast Therapy Response of PDAC Patients.
ISBI
Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann, Daniel Rueckert, Rickmer Braren, Georgios Kaissis
2023 J jnl
Extended Graph Assessment Metrics for Graph Neural Networks.
CoRR
Tamara T. Mueller, Sophie Starck, Leonhard F. Feiner, Kyriaki-Margarita Bintsi, Daniel Rueckert, Georgios Kaissis
2023 — conf
Extended Graph Assessment Metrics for Regression and Weighted Graphs.
GRAIL/OCELOT@MICCAI
Tamara T. Mueller, Sophie Starck, Leonhard F. Feiner, Kyriaki-Margarita Bintsi, Daniel Rueckert, Georgios Kaissis
2023 J jnl
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare.
CoRR
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah, Alejandro F. Frangi, Alena Buyx, Anais Emelie, Andrea Lara, Antonio R. Porras, An-Wen Chan, Arcadi Navarro, Ben Glocker, Benard Ohene Botwe, Bishesh Khanal, Brigit Beger, Carol C. Wu, Celia Cintas, Curtis P. Langlotz, Daniel Rueckert, Deogratias Mzurikwao, Dimitrios I. Fotiadis, Doszhan Zhussupov, Enzo Ferrante, Erik Meijering, Eva Weicken, Fabio A. González, Folkert W. Asselbergs, Fred W. Prior, Gabriel P. Krestin, Gary S. Collins, Geletaw Sahle Tegenaw, Georgios Kaissis, Gianluca Misuraca, Gianna Tsakou, Girish Dwivedi, Haridimos Kondylakis, Harsha Jayakody, Henry C. Woodruff, Hugo J. W. L. Aerts, Ian Walsh, Ioanna Chouvarda, Irène Buvat, Islem Rekik, James S. Duncan, Jayashree Kalpathy-Cramer, Jihad Zahir, Jinah Park, John Mongan, Judy W. Gichoya, Julia A. Schnabel, et al.
2023 — conf
Gradient Self-alignment in Private Deep Learning.
ISIC/Care-AI/MedAGI/DeCaF@MICCAI
David Bani-Harouni, Tamara T. Mueller, Daniel Rueckert, Georgios Kaissis
2023 J jnl
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection.
CoRR
Felix Meissen, Johannes Getzner, Alexander Ziller, Georgios Kaissis, Daniel Rueckert
2023 A* conf
Interactive and Explainable Region-guided Radiology Report Generation.
CVPR
Tim Tanida, Philip Müller, Georgios Kaissis, Daniel Rueckert
2023 J jnl
Interactive and Explainable Region-guided Radiology Report Generation.
CoRR
Tim Tanida, Philip Müller, Georgios Kaissis, Daniel Rueckert
2023 — conf
Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning.
SaTML
Reza Nasirigerdeh, Javad Torkzadehmahani, Daniel Rueckert, Georgios Kaissis
2023 J jnl
Label Noise-Robust Learning using a Confidence-Based Sieving Strategy.
Trans. Mach. Learn. Res.
Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Daniel Rueckert, Georgios Kaissis
2023 J jnl
Leveraging gradient-derived metrics for data selection and valuation in differentially private training.
CoRR
Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
2023 — conf
MAD: Modality Agnostic Distance Measure for Image Registration.
MTSAIL/LEAF/AI4Treat/MMMI/REMIA@MICCAI
Vasiliki Sideri-Lampretsa, Veronika A. Zimmer, Huaqi Qiu, Georgios Kaissis, Daniel Rueckert
2023 J jnl
MAD: Modality Agnostic Distance Measure for Image Registration.
CoRR
Vasiliki Sideri-Lampretsa, Veronika A. Zimmer, Huaqi Qiu, Georgios Kaissis, Daniel Rueckert
2023 — conf
Membership Inference Attacks Against Semantic Segmentation Models.
AISec@CCS
Tomás Chobola, Dmitrii Usynin, Georgios Kaissis
2023 A* conf
Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning.
NeurIPS
Georgios Kaissis, Alexander Ziller, Stefan Kolek, Anneliese Riess, Daniel Rueckert
2023 J jnl
Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy.
CoRR
Soroosh Tayebi Arasteh, Mahshad Lotfinia, Teresa Nolte, Marwin Saehn, Peter Isfort, Christiane Kuhl, Sven Nebelung, Georgios Kaissis, Daniel Truhn
2023 J jnl
Privacy-Utility Trade-offs in Neural Networks for Medical Population Graphs: Insights from Differential Privacy and Graph Structure.
CoRR
Tamara T. Mueller, Maulik Chevli, Ameya Daigavane, Daniel Rueckert, Georgios Kaissis
2023 J jnl
Private, fair and accurate: Training large-scale, privacy-preserving AI models in radiology.
CoRR
Soroosh Tayebi Arasteh, Alexander Ziller, Christiane Kuhl, Marcus R. Makowski, Sven Nebelung, Rickmer Braren, Daniel Rueckert, Daniel Truhn, Georgios Kaissis
2023 — conf
Propagation and Attribution of Uncertainty in Medical Imaging Pipelines.
UNSURE@MICCAI
Leonhard F. Feiner, Martin J. Menten, Kerstin Hammernik, Paul Hager, Wenqi Huang, Daniel Rueckert, Rickmer F. Braren, Georgios Kaissis
2023 J jnl
Propagation and Attribution of Uncertainty in Medical Imaging Pipelines.
CoRR
Leonhard F. Feiner, Martin J. Menten, Kerstin Hammernik, Paul Hager, Wenqi Huang, Daniel Rueckert, Rickmer F. Braren, Georgios Kaissis
2023 J jnl
Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging.
CoRR
Alexander Ziller, Tamara T. Mueller, Simon Stieger, Leonhard F. Feiner, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
2023 — conf
Robust Detection Outcome: A Metric for Pathology Detection in Medical Images.
MIDL
Felix Meissen, Philip Müller, Georgios Kaissis, Daniel Rueckert
2023 J jnl
Robust Detection Outcome: A Metric for Pathology Detection in Medical Images.
CoRR
Felix Meissen, Philip Müller, Georgios Kaissis, Daniel Rueckert
2023 J jnl
SoK: Memorisation in machine learning.
CoRR
Dmitrii Usynin, Moritz Knolle, Georgios Kaissis
2023 J jnl
The Liver Tumor Segmentation Benchmark (LiTS).
Medical Image Anal.
Patrick Bilic, Patrick Ferdinand Christ, Hongwei Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland H. Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivanti, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Miriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Grün, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz K. Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John S. Lowengrub, Jun Ma, Klaus H. Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yue, Liping Zhang, Manuel Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern H. Menze
2023 J jnl
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art.
CoRR
Ioannis Lagogiannis, Felix Meissen, Georgios Kaissis, Daniel Rueckert
2023 — conf
Utility-preserving Federated Learning.
AISec@CCS
Reza Nasirigerdeh, Daniel Rueckert, Georgios Kaissis
2022 J jnl
Author Correction: Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study.
npj Digit. Medicine
Qi Dou, Tiffany Y. So, Meirui Jiang, Quande Liu, Varut Vardhanabhuti, Georgios Kaissis, Zeju Li, Weixin Si, Heather H. C. Lee, Kevin Yu, Zuxin Feng, Li Dong, Egon Burian, Friederike Jungmann, Rickmer Braren, Marcus R. Makowski, Bernhard Kainz, Daniel Rueckert, Ben Glocker, Simon C. H. Yu, Pheng-Ann Heng
2022 J jnl
AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation.
CoRR
Felix Meissen, Georgios Kaissis, Daniel Rueckert
2022 J jnl
Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks.
CoRR
Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
2022 J jnl
Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis.
CoRR
Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis
2022 — conf
Can Collaborative Learning Be Private, Robust and Scalable?
DeCaF/FAIR@MICCAI
Dmitrii Usynin, Helena Klause, Johannes C. Paetzold, Daniel Rueckert, Georgios Kaissis
2022 J jnl
Can collaborative learning be private, robust and scalable?
CoRR
Dmitrii Usynin, Helena Klause, Daniel Rueckert, Georgios Kaissis
2022 J jnl
Differentially Private Graph Classification with GNNs.
CoRR
Tamara T. Mueller, Johannes C. Paetzold, Chinmay Prabhakar, Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
2022 J jnl
Differentially private training of residual networks with scale normalisation.
CoRR
Helena Klause, Alexander Ziller, Daniel Rueckert, Kerstin Hammernik, Georgios Kaissis
2022 J jnl
Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients.
CoRR
Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann, Daniel Rueckert, Rickmer Braren, Georgios Kaissis
2022 J jnl
Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens.
CoRR
Georgios Kaissis, Alexander Ziller, Stefan Kolek Martinez de Azagra, Daniel Rueckert
2022 J jnl
How Do Input Attributes Impact the Privacy Loss in Differential Privacy?
CoRR
Tamara T. Mueller, Stefan Kolek, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Daniel Rueckert, Georgios Kaissis
2022 — conf
Joint Learning of Localized Representations from Medical Images and Reports.
ECCV (26)
Philip Müller, Georgios Kaissis, Congyu Zou, Daniel Rueckert
2022 J jnl
Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning.
CoRR
Reza Nasirigerdeh, Javad Torkzadehmahani, Daniel Rueckert, Georgios Kaissis
2022 J jnl
Kernel Normalized Convolutional Networks.
CoRR
Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Daniel Rueckert, Georgios Kaissis
2022 J jnl
Label Noise-Robust Learning using a Confidence-Based Sieving Strategy.
CoRR
Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Daniel Rueckert, Georgios Kaissis
2022 J jnl
Membership Inference Attacks Against Semantic Segmentation Models.
CoRR
Tomás Chobola, Dmitrii Usynin, Georgios Kaissis
2022 — conf
Multi-Modal Unsupervised Brain Image Registration Using Edge Maps.
ISBI
Vasiliki Sideri-Lampretsa, Georgios Kaissis, Daniel Rueckert
2022 J jnl
Multi-modal unsupervised brain image registration using edge maps.
CoRR
Vasiliki Sideri-Lampretsa, Georgios Kaissis, Daniel Rueckert
2022 — conf
On the Pitfalls of Using the Residual Error as Anomaly Score.
MIDL
Felix Meissen, Benedikt Wiestler, Georgios Kaissis, Daniel Rueckert
2022 J jnl
On the Pitfalls of Using the Residual Error as Anomaly Score.
CoRR
Felix Meissen, Benedikt Wiestler, Georgios Kaissis, Daniel Rueckert
2022 J jnl
Privacy: An Axiomatic Approach.
Entropy
Alexander Ziller, Tamara T. Mueller, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
2022 J jnl
Privacy: An axiomatic approach.
CoRR
Alexander Ziller, Tamara T. Mueller, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
2022 — conf
Radiological Reports Improve Pre-training for Localized Imaging Tasks on Chest X-Rays.
MICCAI (5)
Philip Müller, Georgios Kaissis, Congyu Zou, Daniel Rueckert
2022 — conf
Relationformer: A Unified Framework for Image-to-Graph Generation.
ECCV (37)
Suprosanna Shit, Rajat Koner, Bastian Wittmann, Johannes C. Paetzold, Ivan Ezhov, Hongwei Li, Jiazhen Pan, Sahand Sharifzadeh, Georgios Kaissis, Volker Tresp, Bjoern H. Menze
2022 J jnl
Relationformer: A Unified Framework for Image-to-Graph Generation.
CoRR
Suprosanna Shit, Rajat Koner, Bastian Wittmann, Johannes C. Paetzold, Ivan Ezhov, Hongwei Li, Jiazhen Pan, Sahand Sharifzadeh, Georgios Kaissis, Volker Tresp, Bjoern H. Menze
2022 J jnl
SmoothNets: Optimizing CNN architecture design for differentially private deep learning.
CoRR
Nicolas W. Remerscheid, Alexander Ziller, Daniel Rueckert, Georgios Kaissis
2022 J jnl
SoK: Differential Privacy on Graph-Structured Data.
CoRR
Tamara T. Mueller, Dmitrii Usynin, Johannes C. Paetzold, Daniel Rueckert, Georgios Kaissis
2022 J jnl
The Role of Local Alignment and Uniformity in Image-Text Contrastive Learning on Medical Images.
CoRR
Philip Müller, Georgios Kaissis, Daniel Rueckert
2022 J jnl
Unified Interpretation of the Gaussian Mechanism for Differential Privacy Through the Sensitivity Index.
J. Priv. Confidentiality
Georgios Kaissis, Moritz Knolle, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin, Daniel Rueckert
2022 — conf
Unsupervised Anomaly Localization with Structural Feature-Autoencoders.
BrainLes@MICCAI
Felix Meissen, Johannes C. Paetzold, Georgios Kaissis, Daniel Rueckert
2022 J jnl
Unsupervised Anomaly Localization with Structural Feature-Autoencoders.
CoRR
Felix Meissen, Johannes C. Paetzold, Georgios Kaissis, Daniel Rueckert
2022 J jnl
Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning.
Proc. Priv. Enhancing Technol.
Dmitrii Usynin, Daniel Rueckert, Jonathan Passerat-Palmbach, Georgios Kaissis
2021 J jnl
A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index.
CoRR
Georgios Kaissis, Moritz Knolle, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin, Daniel Rueckert
2021 J jnl
AI reflections in 2020.
Nat. Mach. Intell.
Anna Jobin, Kingson Man, Antonio Damasio, Georgios Kaissis, Rickmer Braren, Julia Stoyanovich, Jay J. Van Bavel, Tessa V. West, Brent D. Mittelstadt, Jason Eshraghian, Marta R. Costa-jussà, Asaf Tzachor, Aimun A. B. Jamjoom, Mariarosaria Taddeo, Edoardo Sinibaldi, Yipeng Hu, Miguel A. Luengo-Oroz
2021 J jnl
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning.
Nat. Mach. Intell.
Dmitrii Usynin, Alexander Ziller, Marcus R. Makowski, Rickmer Braren, Daniel Rueckert, Ben Glocker, Georgios Kaissis, Jonathan Passerat-Palmbach
2021 J jnl
An automatic differentiation system for the age of differential privacy.
CoRR
Dmitrii Usynin, Alexander Ziller, Moritz Knolle, Daniel Rueckert, Georgios Kaissis
2021 — conf
AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation.
MIDOG/MOOD/Learn2Reg@MICCAI
Felix Meissen, Georgios Kaissis, Daniel Rueckert
2021 — conf
Challenging Current Semi-supervised Anomaly Segmentation Methods for Brain MRI.
BrainLes@MICCAI (1)
Felix Meissen, Georgios Kaissis, Daniel Rueckert
2021 — ed.
Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning - 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings
CLIP/DCL/LL-COVID19/PPML@MICCAI
Cristina Oyarzun Laura, M. Jorge Cardoso, Michal Rosen-Zvi, Georgios Kaissis, Marius George Linguraru, Raj Shekhar, Stefan Wesarg, Marius Erdt, Klaus Drechsler, Yufei Chen, Shadi Albarqouni, Spyridon Bakas, Bennett A. Landman, Nicola Rieke, Holger Roth, Xiaoxiao Li, Daguang Xu, Maria Gabrani, Ender Konukoglu, Michal Guindy, Daniel Rueckert, Alexander Ziller, Dmitrii Usynin, Jonathan Passerat-Palmbach
2021 J jnl
Complex-valued deep learning with differential privacy.
CoRR
Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kerstin Hammernik, Daniel Rueckert, Georgios Kaissis
2021 J jnl
Differentially private federated deep learning for multi-site medical image segmentation.
CoRR
Alexander Ziller, Dmitrii Usynin, Nicolas Remerscheid, Moritz Knolle, Marcus R. Makowski, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
2021 J jnl
Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty.
CoRR
Moritz Knolle, Alexander Ziller, Dmitrii Usynin, Rickmer Braren, Marcus R. Makowski, Daniel Rueckert, Georgios Kaissis
2021 J jnl
Distributed Machine Learning and the Semblance of Trust.
CoRR
Dmitrii Usynin, Alexander Ziller, Daniel Rueckert, Jonathan Passerat-Palmbach, Georgios Kaissis
2021 J jnl
End-to-end privacy preserving deep learning on multi-institutional medical imaging.
Nat. Mach. Intell.
Georgios Kaissis, Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel, Dmitrii Usynin, Andrew Trask, Ionésio Lima, Jason Mancuso, Friederike Jungmann, Marc-Matthias Steinborn, Andreas Saleh, Marcus R. Makowski, Daniel Rueckert, Rickmer Braren
2021 J jnl
Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study.
npj Digit. Medicine
Qi Dou, Tiffany Y. So, Meirui Jiang, Quande Liu, Varut Vardhanabhuti, Georgios Kaissis, Zeju Li, Weixin Si, Heather H. C. Lee, Kevin Yu, Zuxin Feng, Li Dong, Egon Burian, Friederike Jungmann, Rickmer Braren, Marcus R. Makowski, Bernhard Kainz, Daniel Rueckert, Ben Glocker, Simon C. H. Yu, Pheng-Ann Heng
2021 J jnl
HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning.
CoRR
Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Julian O. Matschinske, Jan Baumbach, Daniel Rueckert, Georgios Kaissis
2021 J jnl
Joint Learning of Localized Representations from Medical Images and Reports.
CoRR
Philip Müller, Georgios Kaissis, Congyu Zou, Daniel Rueckert
2021 J jnl
Partial sensitivity analysis in differential privacy.
CoRR
Tamara T. Mueller, Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Friederike Jungmann, Daniel Rueckert, Georgios Kaissis
2021 — conf
RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting.
MICCAI (7)
Benjamin Hou, Georgios Kaissis, Ronald M. Summers, Bernhard Kainz
2021 J jnl
RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting.
CoRR
Benjamin Hou, Georgios Kaissis, Ronald M. Summers, Bernhard Kainz
2021 — conf
Segmentation of Peripancreatic Arteries in Multispectral Computed Tomography Imaging.
MLMI@MICCAI
Alina Dima, Johannes C. Paetzold, Friederike Jungmann, Tristan Lemke, Philipp Raffler, Georgios Kaissis, Daniel Rueckert, Rickmer Braren
2021 J jnl
Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation.
CoRR
Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kritika Prakash, Andrew Trask, Rickmer Braren, Marcus R. Makowski, Daniel Rueckert, Georgios Kaissis
2021 B conf
U-Noise: Learnable Noise Masks for Interpretable Image Segmentation.
ICIP
Teddy Koker, Fatemehsadat Mireshghallah, Tom Titcombe, Georgios Kaissis
2021 J jnl
U-Noise: Learnable Noise Masks for Interpretable Image Segmentation.
CoRR
Teddy Koker, Fatemehsadat Mireshghallah, Tom Titcombe, Georgios Kaissis
2021 J jnl
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph).
CoRR
Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit, Ivan Ezhov, Paul Büschl, Chinmay Prabhakar, Mihail I. Todorov, Anjany Sekuboyina, Georgios Kaissis, Ali Ertürk, Stephan Günnemann, Bjoern H. Menze
2021 — conf
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience.
NeurIPS Datasets and Benchmarks
Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit, Ivan Ezhov, Paul Büschl, Chinmay Prabhakar, Anjany Sekuboyina, Mihail I. Todorov, Georgios Kaissis, Ali Ertürk, Stephan Günnemann, Bjoern H. Menze
2020 J jnl
Efficient, high-performance pancreatic segmentation using multi-scale feature extraction.
CoRR
Moritz Knolle, Georgios Kaissis, Friederike Jungmann, Sebastian Ziegelmayer, Daniel Sasse, Marcus R. Makowski, Daniel Rueckert, Rickmer Braren
2020 J jnl
Privacy-preserving medical image analysis.
CoRR
Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel, Dmitrii Usynin, Andrew Trask, Ionésio Da Lima Costa Junior, Jason Mancuso, Marcus R. Makowski, Daniel Rueckert, Rickmer Braren, Georgios Kaissis
2020 J jnl
Secure, privacy-preserving and federated machine learning in medical imaging.
Nat. Mach. Intell.
Georgios Kaissis, Marcus R. Makowski, Daniel Rueckert, Rickmer Braren
2019 J jnl
The Liver Tumor Segmentation Benchmark (LiTS).
CoRR
Patrick Bilic, Patrick Ferdinand Christ, Eugene Vorontsov, Grzegorz Chlebus, Hao Chen, Qi Dou, Chi-Wing Fu, Xiao Han, Pheng-Ann Heng, Jürgen Hesser, Samuel Kadoury, Tomasz K. Konopczynski, Miao Le, Chunming Li, Xiaomeng Li, Jana Lipková, John S. Lowengrub, Hans Meine, Jan Hendrik Moltz, Chris Pal, Marie Piraud, Xiaojuan Qi, Jin Qi, Markus Rempfler, Karsten Roth, Andrea Schenk, Anjany Sekuboyina, Ping Zhou, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Felix Grün, Georgios Kaissis, Fabian Lohöfer, Rickmer Braren, Julian Holch, Felix Hofmann, Wieland H. Sommer, Volker Heinemann, Colin Jacobs, Gabriel Efrain Humpire Mamani, Bram van Ginneken, Gabriel Chartrand, An Tang, Michal Drozdzal, Avi Ben-Cohen, Eyal Klang, Michal Marianne Amitai, Eli Konen, Hayit Greenspan, Johan Moreau, Alexandre Hostettler, Luc Soler, Refael Vivanti, Adi Szeskin, Naama Lev-Cohain, Jacob Sosna, Leo Joskowicz, Bjoern H. Menze
2017 J jnl
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks.
CoRR
Patrick Ferdinand Christ, Florian Ettlinger, Felix Grün, Mohamed Ezzeldin A. Elshaer, Jana Lipková, Sebastian Schlecht, Freba Ahmaddy, Sunil Tatavarty, Marc Bickel, Patrick Bilic, Markus Rempfler, Felix Hofmann, Melvin D'Anastasi, Seyed-Ahmad Ahmadi, Georgios Kaissis, Julian Holch, Wieland H. Sommer, Rickmer Braren, Volker Heinemann, Bjoern H. Menze
2017 — conf
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D Convolutional Neural Networks.
ISBI
Patrick Ferdinand Christ, Florian Ettlinger, Georgios Kaissis, Sebastian Schlecht, Freba Ahmaddy, Felix Grün, Alexander Valentinitsch, Seyed-Ahmad Ahmadi, Rickmer Braren, Bjoern H. Menze
2017 J jnl
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks.
CoRR
Patrick Ferdinand Christ, Florian Ettlinger, Georgios Kaissis, Sebastian Schlecht, Freba Ahmaddy, Felix Grün, Alexander Valentinitsch, Seyed-Ahmad Ahmadi, Rickmer Braren, Bjoern H. Menze