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Georgios Kaissis
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2020 – today
- 2024
- [j19]Daniel Truhn
, Soroosh Tayebi Arasteh
Encrypted federated learning for secure decentralized collaboration in cancer image analysis. Medical Image Anal. 92: 103059 (2024) - [j18]Marieke Ar Bak
Federated learning is not a cure-all for data ethics. Nat. Mac. Intell. 6(4): 370-372 (2024) - [j17]Ioannis Lagogiannis
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art. IEEE Trans. Medical Imaging 43(1): 241-252 (2024) - [j16]Tamara T. Mueller, Sophie Starck, Alina Dima, Stephan Wunderlich, Kyriaki-Margarita Bintsi, Kamilia Zaripova, Rickmer Braren, Daniel Rueckert, Anees Kazi, Georgios Kaissis:
A Survey on Graph Construction for Geometric Deep Learning in Medicine: Methods and Recommendations. Trans. Mach. Learn. Res. 2024 (2024) - [j15]Tamara T. Müller, Sophie Starck, Kyriaki-Margarita Bintsi, Alexander Ziller, Rickmer Braren, Georgios Kaissis, Daniel Rueckert:
Are Population Graphs Really as Powerful as Believed? Trans. Mach. Learn. Res. 2024 (2024) - [j14]Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Daniel Rueckert, Georgios Kaissis:
Kernel Normalized Convolutional Networks. Trans. Mach. Learn. Res. 2024 (2024) - [c34]Philip Müller, Georgios Kaissis, Daniel Rueckert:
ChEX: Interactive Localization and Region Description in Chest X-Rays. ECCV (21) 2024: 92-111 - [c33]Dmitrii Usynin
Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training. EICC 2024: 179-185 - [c32]Georgios Kaissis, Stefan Kolek, Borja Balle, Jamie Hayes, Daniel Rueckert:
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy. ICML 2024 - [c31]Tamara T. Mueller, Maulik Chevli, Ameya Daigavane, Daniel Rueckert, Georgios Kaissis:
Differentially Private Graph Neural Networks for Medical Population Graphs and The Impact of The Graph Structure. ISBI 2024: 1-5 - [c30]Philipp Kaess, Alexander Ziller
Fair and Private CT Contrast Agent Detection. FAIMI/EPIMI@MICCAI 2024: 34-45 - [c29]Deniz Daum, Richard Osuala
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models. DGM4MICCAI@MICCAI 2024: 139-149 - [i71]Philip Müller, Felix Meissen, Georgios Kaissis, Daniel Rueckert:
Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling. CoRR abs/2402.11985 (2024) - [i70]Alexander Ziller, Anneliese Riess, Kristian Schwethelm, Tamara T. Mueller, Daniel Rueckert, Georgios Kaissis:
Bounding Reconstruction Attack Success of Adversaries Without Data Priors. CoRR abs/2402.12861 (2024) - [i69]Alexander H. Berger, Laurin Lux, Suprosanna Shit, Ivan Ezhov, Georgios Kaissis, Martin J. Menten, Daniel Rueckert, Johannes C. Paetzold:
Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers. CoRR abs/2403.06601 (2024) - [i68]Kristian Schwethelm, Johannes Kaiser, Moritz Knolle, Daniel Rueckert, Georgios Kaissis, Alexander Ziller:
Visual Privacy Auditing with Diffusion Models. CoRR abs/2403.07588 (2024) - [i67]Philip Müller, Georgios Kaissis, Daniel Rueckert:
ChEX: Interactive Localization and Region Description in Chest X-rays. CoRR abs/2404.15770 (2024) - [i66]Georgios Kaissis, Stefan Kolek, Borja Balle, Jamie Hayes, Daniel Rueckert:
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy. CoRR abs/2406.08918 (2024) - [i65]Bogdan Kulynych, Juan Felipe Gómez, Georgios Kaissis, Flávio du Pin Calmon, Carmela Troncoso:
Attack-Aware Noise Calibration for Differential Privacy. CoRR abs/2407.02191 (2024) - [i64]Reza Nasirigerdeh, Nader Razmi, Julia A. Schnabel, Daniel Rueckert, Georgios Kaissis:
Machine Unlearning for Medical Imaging. CoRR abs/2407.07539 (2024) - [i63]Richard Osuala, Daniel M. Lang, Anneliese Riess, Georgios Kaissis, Zuzanna Szafranowska, Grzegorz Skorupko, Oliver Díaz, Julia A. Schnabel, Karim Lekadir:
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data. CoRR abs/2407.12669 (2024) - [i62]Deniz Daum, Richard Osuala, Anneliese Riess, Georgios Kaissis, Julia A. Schnabel, Maxime Di Folco:
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models. CoRR abs/2407.16405 (2024) - [i61]Kristian Schwethelm, Johannes Kaiser, Jonas Kuntzer, Mehmet Yigitsoy, Daniel Rueckert, Georgios Kaissis:
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy. CoRR abs/2410.00542 (2024) - 2023
- [j13]Patrick Bilic, Patrick Ferdinand Christ, Hongwei Li
The Liver Tumor Segmentation Benchmark (LiTS). Medical Image Anal. 84: 102680 (2023) - [j12]Tamara T. Mueller
Differentially Private Graph Neural Networks for Whole-Graph Classification. IEEE Trans. Pattern Anal. Mach. Intell. 45(6): 7308-7318 (2023) - [j11]Dmitrii Usynin
Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks. ACM Trans. Priv. Secur. 26(3): 38:1-38:30 (2023) - [j10]Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Daniel Rueckert, Georgios Kaissis:
Label Noise-Robust Learning using a Confidence-Based Sieving Strategy. Trans. Mach. Learn. Res. 2023 (2023) - [c28]Florian A. Hölzl
Equivariant Differentially Private Deep Learning: Why DP-SGD Needs Sparser Models. AISec@CCS 2023: 11-22 - [c27]Tomás Chobola
Membership Inference Attacks Against Semantic Segmentation Models. AISec@CCS 2023: 43-53 - [c26]Reza Nasirigerdeh
Utility-preserving Federated Learning. AISec@CCS 2023: 55-65 - [c25]Tim Tanida, Philip Müller, Georgios Kaissis, Daniel Rueckert:
Interactive and Explainable Region-guided Radiology Report Generation. CVPR 2023: 7433-7442 - [c24]Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann
Exploiting Segmentation Labels and Representation Learning to Forecast Therapy Response of PDAC Patients. ISBI 2023: 1-5 - [c23]Leonhard F. Feiner
Propagation and Attribution of Uncertainty in Medical Imaging Pipelines. UNSURE@MICCAI 2023: 1-11 - [c22]Tamara T. Mueller, Sophie Starck, Leonhard F. Feiner, Kyriaki-Margarita Bintsi, Daniel Rueckert, Georgios Kaissis:
Extended Graph Assessment Metrics for Regression and Weighted Graphs. GRAIL/OCELOT@MICCAI 2023: 14-26 - [c21]Philip Müller, Felix Meissen, Johannes Brandt, Georgios Kaissis, Daniel Rueckert:
Anatomy-Driven Pathology Detection on Chest X-rays. MICCAI (1) 2023: 57-66 - [c20]David Bani-Harouni, Tamara T. Mueller, Daniel Rueckert, Georgios Kaissis:
Gradient Self-alignment in Private Deep Learning. ISIC/Care-AI/MedAGI/DeCaF@MICCAI 2023: 89-97 - [c19]Tamara T. Mueller, Siyu Zhou, Sophie Starck, Friederike Jungmann
Body Fat Estimation from Surface Meshes Using Graph Neural Networks. ShapeMI@MICCAI 2023: 105-117 - [c18]Vasiliki Sideri-Lampretsa, Veronika A. Zimmer, Huaqi Qiu, Georgios Kaissis, Daniel Rueckert:
MAD: Modality Agnostic Distance Measure for Image Registration. MTSAIL/LEAF/AI4Treat/MMMI/REMIA@MICCAI 2023: 147-156 - [c17]Felix Meissen, Philip Müller, Georgios Kaissis, Daniel Rueckert:
Robust Detection Outcome: A Metric for Pathology Detection in Medical Images. MIDL 2023: 568-585 - [c16]Georgios Kaissis, Alexander Ziller, Stefan Kolek, Anneliese Riess, Daniel Rueckert:
Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning. NeurIPS 2023 - [c15]Reza Nasirigerdeh, Javad Torkzadehmahani, Daniel Rueckert, Georgios Kaissis:
Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning. SaTML 2023: 107-118 - [p1]Daniel Rueckert, Moritz Knolle, Nicolas Duchateau, Reza Razavi, Georgios Kaissis:
Diagnosis. AI and Big Data in Cardiology 2023: 85-103 - [i60]Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis:
Equivariant Differentially Private Deep Learning. CoRR abs/2301.13104 (2023) - [i59]Soroosh Tayebi Arasteh, Alexander Ziller, Christiane Kuhl, Marcus R. Makowski, Sven Nebelung, Rickmer Braren, Daniel Rueckert, Daniel Truhn, Georgios Kaissis:
Private, fair and accurate: Training large-scale, privacy-preserving AI models in radiology. CoRR abs/2302.01622 (2023) - [i58]Ioannis Lagogiannis, Felix Meissen, Georgios Kaissis, Daniel Rueckert:
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art. CoRR abs/2303.00609 (2023) - [i57]Felix Meissen, Philip Müller, Georgios Kaissis, Daniel Rueckert:
Robust Detection Outcome: A Metric for Pathology Detection in Medical Images. CoRR abs/2303.01920 (2023) - [i56]Tim Tanida, Philip Müller, Georgios Kaissis, Daniel Rueckert:
Interactive and Explainable Region-guided Radiology Report Generation. CoRR abs/2304.08295 (2023) - [i55]Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis:
Leveraging gradient-derived metrics for data selection and valuation in differentially private training. CoRR abs/2305.02942 (2023) - [i54]Soroosh Tayebi Arasteh, Mahshad Lotfinia, Teresa Nolte, Marwin Saehn, Peter Isfort, Christiane Kuhl, Sven Nebelung, Georgios Kaissis, Daniel Truhn:
Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy. CoRR abs/2306.06503 (2023) - [i53]Georgios Kaissis, Jamie Hayes, Alexander Ziller, Daniel Rueckert:
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy. CoRR abs/2307.03928 (2023) - [i52]Alexander Ziller, Alp Güvenir, Ayhan Can Erdur, Tamara T. Mueller, Philip Müller, Friederike Jungmann, Johannes Brandt, Jan Peeken, Rickmer Braren, Daniel Rueckert, Georgios Kaissis:
Explainable 2D Vision Models for 3D Medical Data. CoRR abs/2307.06614 (2023) - [i51]Tamara T. Mueller, Maulik Chevli, Ameya Daigavane, Daniel Rueckert, Georgios Kaissis:
Privacy-Utility Trade-offs in Neural Networks for Medical Population Graphs: Insights from Differential Privacy and Graph Structure. CoRR abs/2307.06760 (2023) - [i50]Tamara T. Mueller, Sophie Starck, Leonhard F. Feiner, Kyriaki-Margarita Bintsi, Daniel Rueckert, Georgios Kaissis:
Extended Graph Assessment Metrics for Graph Neural Networks. CoRR abs/2307.10112 (2023) - [i49]Tamara T. Mueller, Siyu Zhou, Sophie Starck, Friederike Jungmann, Alexander Ziller, Orhun Aksoy, Danylo Movchan, Rickmer Braren, Georgios Kaissis, Daniel Rueckert:
Body Fat Estimation from Surface Meshes using Graph Neural Networks. CoRR abs/2308.02493 (2023) - [i48]Moritz Knolle, Robert Dorfman, Alexander Ziller, Daniel Rueckert, Georgios Kaissis:
Bias-Aware Minimisation: Understanding and Mitigating Estimator Bias in Private SGD. CoRR abs/2308.12018 (2023) - [i47]Philip Müller, Felix Meissen, Johannes Brandt, Georgios Kaissis, Daniel Rueckert:
Anatomy-Driven Pathology Detection on Chest X-rays. CoRR abs/2309.02578 (2023) - [i46]Vasiliki Sideri-Lampretsa, Veronika A. Zimmer, Huaqi Qiu, Georgios Kaissis, Daniel Rueckert:
MAD: Modality Agnostic Distance Measure for Image Registration. CoRR abs/2309.02875 (2023) - [i45]Karim Lekadir, Aasa Feragen
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare. CoRR abs/2309.12325 (2023) - [i44]Felix Meissen, Svenja Breuer, Moritz Knolle, Alena Buyx, Ruth Müller, Georgios Kaissis, Benedikt Wiestler, Daniel Rueckert:
(Predictable) Performance Bias in Unsupervised Anomaly Detection. CoRR abs/2309.14198 (2023) - [i43]Leonhard F. Feiner, Martin J. Menten, Kerstin Hammernik, Paul Hager, Wenqi Huang, Daniel Rueckert, Rickmer F. Braren, Georgios Kaissis:
Propagation and Attribution of Uncertainty in Medical Imaging Pipelines. CoRR abs/2309.16831 (2023) - [i42]Dmitrii Usynin, Moritz Knolle, Georgios Kaissis:
SoK: Memorisation in machine learning. CoRR abs/2311.03075 (2023) - [i41]Felix Meissen, Johannes Getzner, Alexander Ziller, Georgios Kaissis, Daniel Rueckert:
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection. CoRR abs/2312.03804 (2023) - [i40]Alexander Ziller, Tamara T. Mueller, Simon Stieger, Leonhard F. Feiner, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Georgios Kaissis:
Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging. CoRR abs/2312.04590 (2023) - 2022
- [j9]Alexander Ziller
Privacy: An Axiomatic Approach. Entropy 24(5): 714 (2022) - [j8]Georgios Kaissis
Unified Interpretation of the Gaussian Mechanism for Differential Privacy Through the Sensitivity Index. J. Priv. Confidentiality 12(1) (2022) - [j7]Qi Dou
Author Correction: Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. npj Digit. Medicine 5 (2022) - [j6]Dmitrii Usynin, Daniel Rueckert, Jonathan Passerat-Palmbach, Georgios Kaissis:
Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning. Proc. Priv. Enhancing Technol. 2022(1): 274-290 (2022) - [c14]Felix Meissen, Johannes C. Paetzold, Georgios Kaissis, Daniel Rueckert:
Unsupervised Anomaly Localization with Structural Feature-Autoencoders. BrainLes@MICCAI 2022: 14-24 - [c13]Suprosanna Shit
Relationformer: A Unified Framework for Image-to-Graph Generation. ECCV (37) 2022: 422-439 - [c12]Philip Müller
Joint Learning of Localized Representations from Medical Images and Reports. ECCV (26) 2022: 685-701 - [c11]Vasiliki Sideri-Lampretsa, Georgios Kaissis, Daniel Rueckert:
Multi-Modal Unsupervised Brain Image Registration Using Edge Maps. ISBI 2022: 1-5 - [c10]Dmitrii Usynin, Helena Klause, Johannes C. Paetzold, Daniel Rueckert, Georgios Kaissis:
Can Collaborative Learning Be Private, Robust and Scalable? DeCaF/FAIR@MICCAI 2022: 37-46 - [c9]Philip Müller
Radiological Reports Improve Pre-training for Localized Imaging Tasks on Chest X-Rays. MICCAI (5) 2022: 647-657 - [c8]Felix Meissen, Benedikt Wiestler, Georgios Kaissis, Daniel Rueckert:
On the Pitfalls of Using the Residual Error as Anomaly Score. MIDL 2022: 914-928 - [i39]Felix Meissen, Georgios Kaissis, Daniel Rueckert:
AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation. CoRR abs/2201.09579 (2022) - [i38]Tamara T. Mueller, Johannes C. Paetzold, Chinmay Prabhakar, Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis:
Differentially Private Graph Classification with GNNs. CoRR abs/2202.02575 (2022) - [i37]Felix Meissen, Benedikt Wiestler, Georgios Kaissis, Daniel Rueckert:
On the Pitfalls of Using the Residual Error as Anomaly Score. CoRR abs/2202.03826 (2022) - [i36]Vasiliki Sideri-Lampretsa, Georgios Kaissis, Daniel Rueckert:
Multi-modal unsupervised brain image registration using edge maps. CoRR abs/2202.04647 (2022) - [i35]Helena Klause, Alexander Ziller, Daniel Rueckert, Kerstin Hammernik, Georgios Kaissis:
Differentially private training of residual networks with scale normalisation. CoRR abs/2203.00324 (2022) - [i34]Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis:
Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks. CoRR abs/2203.00481 (2022) - [i33]Tamara T. Mueller, Dmitrii Usynin, Johannes C. Paetzold, Daniel Rueckert, Georgios Kaissis:
SoK: Differential Privacy on Graph-Structured Data. CoRR abs/2203.09205 (2022) - [i32]Suprosanna Shit, Rajat Koner, Bastian Wittmann, Johannes C. Paetzold, Ivan Ezhov, Hongwei Li, Jiazhen Pan, Sahand Sharifzadeh, Georgios Kaissis, Volker Tresp, Bjoern H. Menze:
Relationformer: A Unified Framework for Image-to-Graph Generation. CoRR abs/2203.10202 (2022) - [i31]Alexander Ziller, Tamara T. Mueller, Rickmer Braren, Daniel Rueckert, Georgios Kaissis:
Privacy: An axiomatic approach. CoRR abs/2203.11586 (2022) - [i30]Dmitrii Usynin, Helena Klause, Daniel Rueckert, Georgios Kaissis:
Can collaborative learning be private, robust and scalable? CoRR abs/2205.02652 (2022) - [i29]Nicolas W. Remerscheid, Alexander Ziller, Daniel Rueckert, Georgios Kaissis:
SmoothNets: Optimizing CNN architecture design for differentially private deep learning. CoRR abs/2205.04095 (2022) - [i28]Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Daniel Rueckert, Georgios Kaissis:
Kernel Normalized Convolutional Networks. CoRR abs/2205.10089 (2022) - [i27]Felix Meissen, Johannes C. Paetzold, Georgios Kaissis, Daniel Rueckert:
Unsupervised Anomaly Localization with Structural Feature-Autoencoders. CoRR abs/2208.10992 (2022) - [i26]Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis:
Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis. CoRR abs/2209.04338 (2022) - [i25]Reza Nasirigerdeh, Javad Torkzadehmahani, Daniel Rueckert, Georgios Kaissis:
Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning. CoRR abs/2210.00053 (2022) - [i24]Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Daniel Rueckert, Georgios Kaissis:
Label Noise-Robust Learning using a Confidence-Based Sieving Strategy. CoRR abs/2210.05330 (2022) - [i23]Georgios Kaissis, Alexander Ziller, Stefan Kolek Martinez de Azagra, Daniel Rueckert:
Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens. CoRR abs/2210.13028 (2022) - [i22]Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann, Daniel Rueckert, Rickmer Braren, Georgios Kaissis:
Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients. CoRR abs/2211.04180 (2022) - [i21]Philip Müller, Georgios Kaissis, Daniel Rueckert:
The Role of Local Alignment and Uniformity in Image-Text Contrastive Learning on Medical Images. CoRR abs/2211.07254 (2022) - [i20]Tamara T. Mueller, Stefan Kolek, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Daniel Rueckert, Georgios Kaissis:
How Do Input Attributes Impact the Privacy Loss in Differential Privacy? CoRR abs/2211.10173 (2022) - [i19]Tomás Chobola, Dmitrii Usynin, Georgios Kaissis:
Membership Inference Attacks Against Semantic Segmentation Models. CoRR abs/2212.01082 (2022) - 2021
- [j5]Anna Jobin, Kingson Man, Antonio Damasio, Georgios Kaissis, Rickmer Braren, Julia Stoyanovich, Jay J. Van Bavel, Tessa V. West, Brent D. Mittelstadt, Jason Eshraghian
AI reflections in 2020. Nat. Mach. Intell. 3(1): 2-8 (2021) - [j4]Georgios Kaissis
End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nat. Mach. Intell. 3(6): 473-484 (2021) - [j3]Dmitrii Usynin
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning. Nat. Mach. Intell. 3(9): 749-758 (2021) - [j2]Qi Dou
Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. npj Digit. Medicine 4 (2021) - [c7]Felix Meissen, Georgios Kaissis, Daniel Rueckert:
Challenging Current Semi-supervised Anomaly Segmentation Methods for Brain MRI. BrainLes@MICCAI (1) 2021: 63-74 - [c6]Teddy Koker, Fatemehsadat Mireshghallah, Tom Titcombe, Georgios Kaissis:
U-Noise: Learnable Noise Masks for Interpretable Image Segmentation. ICIP 2021: 394-398 - [c5]Felix Meissen, Georgios Kaissis, Daniel Rueckert:
AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation. MIDOG/MOOD/Learn2Reg@MICCAI 2021: 127-135 - [c4]Benjamin Hou, Georgios Kaissis, Ronald M. Summers, Bernhard Kainz
RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting. MICCAI (7) 2021: 293-303 - [c3]Alina Dima, Johannes C. Paetzold, Friederike Jungmann
Segmentation of Peripancreatic Arteries in Multispectral Computed Tomography Imaging. MLMI@MICCAI 2021: 596-605 - [c2]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:
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience. NeurIPS Datasets and Benchmarks 2021 - [e1]Cristina Oyarzun Laura, M. Jorge Cardoso
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. Lecture Notes in Computer Science 12969, Springer 2021, ISBN 978-3-030-90873-7 [contents] - [i18]Teddy Koker, Fatemehsadat Mireshghallah, Tom Titcombe, Georgios Kaissis:
U-Noise: Learnable Noise Masks for Interpretable Image Segmentation. CoRR abs/2101.05791 (2021) - [i17]Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Julian O. Matschinske, Jan Baumbach, Daniel Rueckert, Georgios Kaissis:
HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning. CoRR abs/2105.10545 (2021) - [i16]Benjamin Hou, Georgios Kaissis, Ronald M. Summers, Bernhard Kainz:
RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting. CoRR abs/2107.02104 (2021) - [i15]Alexander Ziller, Dmitrii Usynin
Differentially private federated deep learning for multi-site medical image segmentation. CoRR abs/2107.02586 (2021) - [i14]Alexander Ziller, Dmitrii Usynin
Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation. CoRR abs/2107.04265 (2021) - [i13]Moritz Knolle, Alexander Ziller, Dmitrii Usynin
Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty. CoRR abs/2107.04296 (2021) - [i12]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:
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph). CoRR abs/2108.13233 (2021) - [i11]Georgios Kaissis, Moritz Knolle, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin
A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index. CoRR abs/2109.10528 (2021) - [i10]Dmitrii Usynin
An automatic differentiation system for the age of differential privacy. CoRR abs/2109.10573 (2021) - [i9]Tamara T. Mueller, Alexander Ziller, Dmitrii Usynin
Partial sensitivity analysis in differential privacy. CoRR abs/2109.10582 (2021) - [i8]Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kerstin Hammernik, Daniel Rueckert, Georgios Kaissis:
Complex-valued deep learning with differential privacy. CoRR abs/2110.03478 (2021) - [i7]Philip Müller
Joint Learning of Localized Representations from Medical Images and Reports. CoRR abs/2112.02889 (2021) - [i6]Dmitrii Usynin, Alexander Ziller, Daniel Rueckert, Jonathan Passerat-Palmbach, Georgios Kaissis:
Distributed Machine Learning and the Semblance of Trust. CoRR abs/2112.11040 (2021) - 2020
- [j1]Georgios Kaissis, Marcus R. Makowski, Daniel Rueckert
Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2(6): 305-311 (2020) - [i5]Moritz Knolle, Georgios Kaissis, Friederike Jungmann, Sebastian Ziegelmayer, Daniel Sasse, Marcus R. Makowski, Daniel Rueckert, Rickmer Braren:
Efficient, high-performance pancreatic segmentation using multi-scale feature extraction. CoRR abs/2009.00872 (2020) - [i4]Alexander Ziller
Privacy-preserving medical image analysis. CoRR abs/2012.06354 (2020)
2010 – 2019
- 2019
- [i3]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
The Liver Tumor Segmentation Benchmark (LiTS). CoRR abs/1901.04056 (2019) - 2017
- [c1]Patrick Ferdinand Christ, Florian Ettlinger, Georgios Kaissis, Sebastian Schlecht, Freba Ahmaddy, Felix Grün, Alexander Valentinitsch, Seyed-Ahmad Ahmadi
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D Convolutional Neural Networks. ISBI 2017: 839-843 - [i2]Patrick Ferdinand Christ, Florian Ettlinger, Georgios Kaissis, Sebastian Schlecht, Freba Ahmaddy, Felix Grün, Alexander Valentinitsch, Seyed-Ahmad Ahmadi, Rickmer Braren, Bjoern H. Menze:
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks. CoRR abs/1702.05941 (2017) - [i1]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:
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks. CoRR abs/1702.05970 (2017)
Coauthor Index
Rickmer Braren
aka: Rickmer F. Braren
aka: Rickmer F. Braren
Tamara T. Mueller
aka: Tamara T. Müller
aka: Tamara T. Müller
[j18] [j17] [j16] [j15] [j14] [c34] [c33] [c32] [c31] [c30] [i71] [i70] [i69] [i68] [i67] [i66] [i64] [i61] [j12] [j11] [j10] [c28] [c26] [c25] [c24] [c23] [c22] [c21] [c20] [c19] [c18] [c17] [c16] [c15] [p1] [i60] [i59] [i58] [i57] [i56] [i55] [i53] [i52] [i51] [i50] [i49] [i48] [i47] [i46] [i45] [i44] [i43] [i41] [i40] [j9] [j8] [j7] [j6] [c14] [c12] [c11] [c10] [c9] [c8] [i39] [i38] [i37] [i36] [i35] [i34] [i33] [i31] [i30] [i29] [i28] [i27] [i26] [i25] [i24] [i23] [i22] [i21] [i20] [j4] [j3] [j2] [c7] [c5] [c3] [e1] [i17] [i15] [i14] [i13] [i11] [i10] [i9] [i8] [i7] [i6] [j1] [i5] [i4]
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