Georgios Kaissis
Orcid: 0000-0001-8382-8062
According to our database1,
Georgios Kaissis
authored at least 98 papers
between 2017 and 2024.
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Bibliography
2024
Encrypted federated learning for secure decentralized collaboration in cancer image analysis.
Medical Image Anal., February, 2024
IEEE Trans. Medical Imaging, January, 2024
A Survey on Graph Construction for Geometric Deep Learning in Medicine: Methods and Recommendations.
Trans. Mach. Learn. Res., 2024
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy.
CoRR, 2024
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data.
CoRR, 2024
CoRR, 2024
CoRR, 2024
Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling.
CoRR, 2024
Proceedings of the Ethics and Fairness in Medical Imaging, 2024
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models.
Proceedings of the Deep Generative Models - 4th MICCAI Workshop, 2024
Differentially Private Graph Neural Networks for Medical Population Graphs and The Impact of The Graph Structure.
Proceedings of the IEEE International Symposium on Biomedical Imaging, 2024
Proceedings of the Forty-first International Conference on Machine Learning, 2024
Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training.
Proceedings of the European Interdisciplinary Cybersecurity Conference, 2024
Proceedings of the Computer Vision - ECCV 2024, 2024
2023
ACM Trans. Priv. Secur., August, 2023
IEEE Trans. Pattern Anal. Mach. Intell., June, 2023
Trans. Mach. Learn. Res., 2023
CoRR, 2023
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection.
CoRR, 2023
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare.
CoRR, 2023
CoRR, 2023
Privacy-Utility Trade-offs in Neural Networks for Medical Population Graphs: Insights from Differential Privacy and Graph Structure.
CoRR, 2023
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy.
CoRR, 2023
Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy.
CoRR, 2023
Leveraging gradient-derived metrics for data selection and valuation in differentially private training.
CoRR, 2023
Private, fair and accurate: Training large-scale, privacy-preserving AI models in radiology.
CoRR, 2023
Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning, 2023
Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the Medical Imaging with Deep Learning, 2023
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops, 2023
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023
Proceedings of the Shape in Medical Imaging - International Workshop, 2023
Proceedings of the Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology, 2023
Proceedings of the Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, 2023
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops, 2023
Exploiting Segmentation Labels and Representation Learning to Forecast Therapy Response of PDAC Patients.
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, 2023
Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, 2023
Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, 2023
2022
Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning.
Proc. Priv. Enhancing Technol., 2022
Author Correction: Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study.
npj Digit. Medicine, 2022
Unified Interpretation of the Gaussian Mechanism for Differential Privacy Through the Sensitivity Index.
J. Priv. Confidentiality, 2022
The Role of Local Alignment and Uniformity in Image-Text Contrastive Learning on Medical Images.
CoRR, 2022
Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens.
CoRR, 2022
Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis.
CoRR, 2022
SmoothNets: Optimizing CNN architecture design for differentially private deep learning.
CoRR, 2022
CoRR, 2022
Proceedings of the International Conference on Medical Imaging with Deep Learning, 2022
Proceedings of the Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health, 2022
Radiological Reports Improve Pre-training for Localized Imaging Tasks on Chest X-Rays.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022
Proceedings of the 19th IEEE International Symposium on Biomedical Imaging, 2022
Proceedings of the Computer Vision - ECCV 2022, 2022
Proceedings of the Computer Vision - ECCV 2022, 2022
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2022
2021
Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study.
npj Digit. Medicine, 2021
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning.
Nat. Mach. Intell., 2021
Nat. Mach. Intell., 2021
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph).
CoRR, 2021
Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty.
CoRR, 2021
Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation.
CoRR, 2021
Differentially private federated deep learning for multi-site medical image segmentation.
CoRR, 2021
CoRR, 2021
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021
Proceedings of the Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis - MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021
Segmentation of Peripancreatic Arteries in Multispectral Computed Tomography Imaging.
Proceedings of the Machine Learning in Medical Imaging - 12th International Workshop, 2021
Proceedings of the 2021 IEEE International Conference on Image Processing, 2021
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2021
2020
Nat. Mach. Intell., 2020
Efficient, high-performance pancreatic segmentation using multi-scale feature extraction.
CoRR, 2020
2019
2017
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks.
CoRR, 2017
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D Convolutional Neural Networks.
Proceedings of the 14th IEEE International Symposium on Biomedical Imaging, 2017