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Picture for Georgios Kaissis

Georgios Kaissis

Department of diagnostic and interventional Radiology, Technical University of Munich, Munich, Germany, Institute for Artificial Intelligence and Data Science in Medicine and Healthcare, Technical University of Munich, Munich, Germany, OpenMined Research, Department of Computing, Imperial College London, London, United Kingdom

Complex-valued deep learning with differential privacy


Oct 07, 2021
Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kerstin Hammernik, Daniel Rueckert, Georgios Kaissis

* Submitted as conference paper to ICLR 2022 

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Partial sensitivity analysis in differential privacy


Sep 22, 2021
Tamara T. Mueller, Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Friederike Jungmann, Daniel Rueckert, Georgios Kaissis


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An automatic differentiation system for the age of differential privacy


Sep 22, 2021
Dmitrii Usynin, Alexander Ziller, Moritz Knolle, Daniel Rueckert, Georgios Kaissis

* 8 pages 

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A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index


Sep 22, 2021
Georgios Kaissis, Moritz Knolle, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin, Daniel Rueckert

* Under review at PETS 2022 

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Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI


Sep 13, 2021
Felix Meissen, Georgios Kaissis, Daniel Rueckert

* 10 pages, 4 figures, accepted to the MICCAI 2021 BrainLes Workshop 

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Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)


Aug 30, 2021
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


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NeuralDP Differentially private neural networks by design


Aug 10, 2021
Moritz Knolle, Dmitrii Usynin, Alexander Ziller, Marcus R. Makowski, Daniel Rueckert, Georgios Kaissis

* Paper withdrawn. The paper contains a factual error 

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Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty


Aug 04, 2021
Moritz Knolle, Alexander Ziller, Dmitrii Usynin, Rickmer Braren, Marcus R. Makowski, Daniel Rueckert, Georgios Kaissis

* Accepted to the ICML 2021 Theory and Practice of Differential Privacy Workshop 

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Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty


Jul 09, 2021
Moritz Knolle, Alexander Ziller, Dmitrii Usynin, Rickmer Braren, Marcus R. Makowski, Daniel Rueckert, Georgios Kaissis

* Accepted to the ICML 2021 Theory and Practice of Differential Privacy Workshop 

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Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation


Jul 09, 2021
Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kritika Prakash, Andrew Trask, Rickmer Braren, Marcus Makowski, Daniel Rueckert, Georgios Kaissis

* Accepted to the ICML 2021 Theory and Practice of Differential Privacy Workshop 

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Differentially private federated deep learning for multi-site medical image segmentation


Jul 06, 2021
Alexander Ziller, Dmitrii Usynin, Nicolas Remerscheid, Moritz Knolle, Marcus Makowski, Rickmer Braren, Daniel Rueckert, Georgios Kaissis

* Submitted to the Journal of Machine Learning in Biomedical Imaging (MELBA) 

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RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting


Jul 05, 2021
Benjamin Hou, Georgios Kaissis, Ronald Summers, Bernhard Kainz


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HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning


May 21, 2021
Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Julian Matschinske, Jan Baumbach, Daniel Rueckert, Georgios Kaissis


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U-Noise: Learnable Noise Masks for Interpretable Image Segmentation


Jan 20, 2021
Teddy Koker, Fatemehsadat Mireshghallah, Tom Titcombe, Georgios Kaissis

* Submitted to ICIP. Revision: corrected affiliation 

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Privacy-preserving medical image analysis


Dec 10, 2020
Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel, Dmitrii Usynin, Andrew Trask, Ionésio Da Lima Costa Junior, Jason Mancuso, Marcus Makowski, Daniel Rueckert, Rickmer Braren, Georgios Kaissis

* Accepted at the workshop for Medical Imaging meets NeurIPS, 34th Conference on Neural Information Processing Systems (NeurIPS) December 11, 2020 

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Efficient, high-performance pancreatic segmentation using multi-scale feature extraction


Sep 02, 2020
Moritz Knolle, Georgios Kaissis, Friederike Jungmann, Sebastian Ziegelmayer, Daniel Sasse, Marcus Makowski, Daniel Rueckert, Rickmer Braren


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The Liver Tumor Segmentation Benchmark (LiTS)


Jan 13, 2019
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 Konopczynski, Miao Le, Chunming Li, Xiaomeng Li, Jana Lipkovà, John Lowengrub, Hans Meine, Jan Hendrik Moltz, Chris Pal, Marie Piraud, Xiaojuan Qi, Jin Qi, Markus Rempfler, Karsten Roth, Andrea Schenk, Anjany Sekuboyina, Eugene Vorontsov, Ping Zhou, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Felix Gruen, Georgios Kaissis, Fabian Lohöfer, Rickmer Braren, Julian Holch, Felix Hofmann, Wieland Sommer, Volker Heinemann, Colin Jacobs, Gabriel Efrain Humpire Mamani, Bram van Ginneken, Gabriel Chartrand, An Tang, Michal Drozdzal, Avi Ben-Cohen, Eyal Klang, Marianne M. 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

* conference 

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Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks


Feb 23, 2017
Patrick Ferdinand Christ, Florian Ettlinger, Felix Grün, Mohamed Ezzeldin A. Elshaera, Jana Lipkova, 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 Sommer, Rickmer Braren, Volker Heinemann, Bjoern Menze

* Under Review 

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SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks


Feb 20, 2017
Patrick Ferdinand Christ, Florian Ettlinger, Georgios Kaissis, Sebastian Schlecht, Freba Ahmaddy, Felix Grün, Alexander Valentinitsch, Seyed-Ahmad Ahmadi, Rickmer Braren, Bjoern Menze

* Accepted at IEEE ISBI 2017 

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