Georgios Kaissis

According to our database, Georgios Kaissis authored at least 85 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Encrypted federated learning for secure decentralized collaboration in cancer image analysis.

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

Medical Image Anal., February, 2024

Unsupervised Pathology Detection: A Deep Dive Into the State of the Art.

Ioannis Lagogiannis, Felix Meissen, Georgios Kaissis, Daniel Rueckert

IEEE Trans. Medical Imaging, January, 2024

Visual Privacy Auditing with Diffusion Models.

Kristian Schwethelm, Johannes Kaiser, Moritz Knolle, Daniel Rueckert, Georgios Kaissis, Alexander Ziller

CoRR, 2024

Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers.

Alexander H. Berger, Laurin Lux, Suprosanna Shit, Ivan Ezhov, Georgios Kaissis, Martin J. Menten, Daniel Rueckert, Johannes C. Paetzold

CoRR, 2024

Bounding Reconstruction Attack Success of Adversaries Without Data Priors.

Alexander Ziller, Anneliese Riess, Kristian Schwethelm, Tamara T. Mueller, Daniel Rueckert, Georgios Kaissis

CoRR, 2024

Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling.

Philip Müller, Felix Meissen, Georgios Kaissis, Daniel Rueckert

CoRR, 2024

2023

Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks.

Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis

ACM Trans. Priv. Secur., August, 2023

Differentially Private Graph Neural Networks for Whole-Graph Classification.

Tamara T. Mueller, Johannes C. Paetzold, Chinmay Prabhakar, Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis

IEEE Trans. Pattern Anal. Mach. Intell., June, 2023

The Liver Tumor Segmentation Benchmark (LiTS).

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

Medical Image Anal., 2023

Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging.

Alexander Ziller, Tamara T. Mueller, Simon Stieger, Leonhard F. Feiner, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Georgios Kaissis

CoRR, 2023

How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection.

Felix Meissen, Johannes Getzner, Alexander Ziller, Georgios Kaissis, Daniel Rueckert

CoRR, 2023

SoK: Memorisation in machine learning.

Dmitrii Usynin, Moritz Knolle, Georgios Kaissis

CoRR, 2023

(Predictable) Performance Bias in Unsupervised Anomaly Detection.

Felix Meissen, Svenja Breuer, Moritz Knolle, Alena Buyx, Ruth Müller, Georgios Kaissis, Benedikt Wiestler, Daniel Rueckert

CoRR, 2023

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare.

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.

CoRR, 2023

Bias-Aware Minimisation: Understanding and Mitigating Estimator Bias in Private SGD.

Moritz Knolle, Robert Dorfman, Alexander Ziller, Daniel Rueckert, Georgios Kaissis

CoRR, 2023

Extended Graph Assessment Metrics for Graph Neural Networks.

Tamara T. Mueller, Sophie Starck, Leonhard F. Feiner, Kyriaki-Margarita Bintsi, Daniel Rueckert, Georgios Kaissis

CoRR, 2023

Privacy-Utility Trade-offs in Neural Networks for Medical Population Graphs: Insights from Differential Privacy and Graph Structure.

Tamara T. Mueller, Maulik Chevli, Ameya Daigavane, Daniel Rueckert, Georgios Kaissis

CoRR, 2023

Explainable 2D Vision Models for 3D Medical Data.

Alexander Ziller, Alp Güvenir, Ayhan Can Erdur, Tamara T. Mueller, Philip Müller, Friederike Jungmann, Johannes Brandt, Jan Peeken, Rickmer Braren, Daniel Rueckert, Georgios Kaissis

CoRR, 2023

Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy.

Georgios Kaissis, Jamie Hayes, Alexander Ziller, Daniel Rueckert

CoRR, 2023

Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy.

Soroosh Tayebi Arasteh, Mahshad Lotfinia, Teresa Nolte, Marwin Saehn, Peter Isfort, Christiane Kuhl, Sven Nebelung, Georgios Kaissis, Daniel Truhn

CoRR, 2023

Leveraging gradient-derived metrics for data selection and valuation in differentially private training.

Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis

CoRR, 2023

Private, fair and accurate: Training large-scale, privacy-preserving AI models in radiology.

Soroosh Tayebi Arasteh, Alexander Ziller, Christiane Kuhl, Marcus R. Makowski, Sven Nebelung, Rickmer Braren, Daniel Rueckert, Daniel Truhn, Georgios Kaissis

CoRR, 2023

Equivariant Differentially Private Deep Learning.

Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis

CoRR, 2023

Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning.

Georgios Kaissis, Alexander Ziller, Stefan Kolek, Anneliese Riess, Daniel Rueckert

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Robust Detection Outcome: A Metric for Pathology Detection in Medical Images.

Felix Meissen, Philip Müller, Georgios Kaissis, Daniel Rueckert

Proceedings of the Medical Imaging with Deep Learning, 2023

MAD: Modality Agnostic Distance Measure for Image Registration.

Vasiliki Sideri-Lampretsa, Veronika A. Zimmer, Huaqi Qiu, Georgios Kaissis, Daniel Rueckert

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops, 2023

Anatomy-Driven Pathology Detection on Chest X-rays.

Philip Müller, Felix Meissen, Johannes Brandt, Georgios Kaissis, Daniel Rueckert

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

Body Fat Estimation from Surface Meshes Using Graph Neural Networks.

Tamara T. Mueller, Siyu Zhou, Sophie Starck, Friederike Jungmann, Alexander Ziller, Orhun Aksoy, Danylo Movchan, Rickmer Braren, Georgios Kaissis, Daniel Rueckert

Proceedings of the Shape in Medical Imaging - International Workshop, 2023

Extended Graph Assessment Metrics for Regression and Weighted Graphs.

Tamara T. Mueller, Sophie Starck, Leonhard F. Feiner, Kyriaki-Margarita Bintsi, Daniel Rueckert, Georgios Kaissis

Proceedings of the Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology, 2023

Propagation and Attribution of Uncertainty in Medical Imaging Pipelines.

Leonhard F. Feiner, Martin J. Menten, Kerstin Hammernik, Paul Hager, Wenqi Huang, Daniel Rueckert, Rickmer F. Braren, Georgios Kaissis

Proceedings of the Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, 2023

Gradient Self-alignment in Private Deep Learning.

David Bani-Harouni, Tamara T. Mueller, Daniel Rueckert, Georgios Kaissis

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.

Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann, Daniel Rueckert, Rickmer Braren, Georgios Kaissis

Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

Interactive and Explainable Region-guided Radiology Report Generation.

Tim Tanida, Philip Müller, Georgios Kaissis, Daniel Rueckert

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Utility-preserving Federated Learning.

Reza Nasirigerdeh, Daniel Rueckert, Georgios Kaissis

Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, 2023

Equivariant Differentially Private Deep Learning: Why DP-SGD Needs Sparser Models.

Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis

Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, 2023

Membership Inference Attacks Against Semantic Segmentation Models.

Tomás Chobola, Dmitrii Usynin, Georgios Kaissis

Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, 2023

Diagnosis.

Daniel Rueckert, Moritz Knolle, Nicolas Duchateau, Reza Razavi, Georgios Kaissis

Proceedings of the AI and Big Data in Cardiology: A Practical Guide, 2023

2022

Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning.

Dmitrii Usynin, Daniel Rueckert, Jonathan Passerat-Palmbach, Georgios Kaissis

Proc. Priv. Enhancing Technol., 2022

Author Correction: Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study.

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

npj Digit. Medicine, 2022

Unified Interpretation of the Gaussian Mechanism for Differential Privacy Through the Sensitivity Index.

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

J. Priv. Confidentiality, 2022

Privacy: An Axiomatic Approach.

Alexander Ziller, Tamara T. Mueller, Rickmer Braren, Daniel Rueckert, Georgios Kaissis

Entropy, 2022

How Do Input Attributes Impact the Privacy Loss in Differential Privacy?

Tamara T. Mueller, Stefan Kolek, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Daniel Rueckert, Georgios Kaissis

CoRR, 2022

The Role of Local Alignment and Uniformity in Image-Text Contrastive Learning on Medical Images.

Philip Müller, Georgios Kaissis, Daniel Rueckert

CoRR, 2022

Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens.

Georgios Kaissis, Alexander Ziller, Stefan Kolek Martinez de Azagra, Daniel Rueckert

CoRR, 2022

Label Noise-Robust Learning using a Confidence-Based Sieving Strategy.

Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Daniel Rueckert, Georgios Kaissis

CoRR, 2022

Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning.

Reza Nasirigerdeh, Javad Torkzadehmahani, Daniel Rueckert, Georgios Kaissis

CoRR, 2022

Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis.

Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis

CoRR, 2022

Kernel Normalized Convolutional Networks.

Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Daniel Rueckert, Georgios Kaissis

CoRR, 2022

SmoothNets: Optimizing CNN architecture design for differentially private deep learning.

Nicolas W. Remerscheid, Alexander Ziller, Daniel Rueckert, Georgios Kaissis

CoRR, 2022

Can collaborative learning be private, robust and scalable?

Dmitrii Usynin, Helena Klause, Daniel Rueckert, Georgios Kaissis

CoRR, 2022

SoK: Differential Privacy on Graph-Structured Data.

Tamara T. Mueller, Dmitrii Usynin, Johannes C. Paetzold, Daniel Rueckert, Georgios Kaissis

CoRR, 2022

Differentially private training of residual networks with scale normalisation.

Helena Klause, Alexander Ziller, Daniel Rueckert, Kerstin Hammernik, Georgios Kaissis

CoRR, 2022

Differentially Private Graph Classification with GNNs.

Tamara T. Mueller, Johannes C. Paetzold, Chinmay Prabhakar, Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis

CoRR, 2022

On the Pitfalls of Using the Residual Error as Anomaly Score.

Felix Meissen, Benedikt Wiestler, Georgios Kaissis, Daniel Rueckert

Proceedings of the International Conference on Medical Imaging with Deep Learning, 2022

Can Collaborative Learning Be Private, Robust and Scalable?

Dmitrii Usynin, Helena Klause, Johannes C. Paetzold, Daniel Rueckert, Georgios Kaissis

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.

Philip Müller, Georgios Kaissis, Congyu Zou, Daniel Rueckert

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

Multi-Modal Unsupervised Brain Image Registration Using Edge Maps.

Vasiliki Sideri-Lampretsa, Georgios Kaissis, Daniel Rueckert

Proceedings of the 19th IEEE International Symposium on Biomedical Imaging, 2022

Relationformer: A Unified Framework for Image-to-Graph Generation.

Suprosanna Shit, Rajat Koner, Bastian Wittmann, Johannes C. Paetzold, Ivan Ezhov, Hongwei Li, Jiazhen Pan, Sahand Sharifzadeh, Georgios Kaissis, Volker Tresp, Bjoern H. Menze

Proceedings of the Computer Vision - ECCV 2022, 2022

Joint Learning of Localized Representations from Medical Images and Reports.

Philip Müller, Georgios Kaissis, Congyu Zou, Daniel Rueckert

Proceedings of the Computer Vision - ECCV 2022, 2022

Unsupervised Anomaly Localization with Structural Feature-Autoencoders.

Felix Meissen, Johannes C. Paetzold, Georgios Kaissis, Daniel Rueckert

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.

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

npj Digit. Medicine, 2021

Adversarial interference and its mitigations in privacy-preserving collaborative machine learning.

Dmitrii Usynin, Alexander Ziller, Marcus R. Makowski, Rickmer Braren, Daniel Rueckert, Ben Glocker, Georgios Kaissis, Jonathan Passerat-Palmbach

Nat. Mach. Intell., 2021

End-to-end privacy preserving deep learning on multi-institutional medical imaging.

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

Nat. Mach. Intell., 2021

AI reflections in 2020.

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

Nat. Mach. Intell., 2021

Distributed Machine Learning and the Semblance of Trust.

Dmitrii Usynin, Alexander Ziller, Daniel Rueckert, Jonathan Passerat-Palmbach, Georgios Kaissis

CoRR, 2021

Complex-valued deep learning with differential privacy.

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

CoRR, 2021

Partial sensitivity analysis in differential privacy.

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

CoRR, 2021

An automatic differentiation system for the age of differential privacy.

Dmitrii Usynin, Alexander Ziller, Moritz Knolle, Daniel Rueckert, Georgios Kaissis

CoRR, 2021

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

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

CoRR, 2021

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

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

CoRR, 2021

Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation.

Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kritika Prakash, Andrew Trask, Rickmer Braren, Marcus R. Makowski, Daniel Rueckert, Georgios Kaissis

CoRR, 2021

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

Alexander Ziller, Dmitrii Usynin, Nicolas Remerscheid, Moritz Knolle, Marcus R. Makowski, Rickmer Braren, Daniel Rueckert, Georgios Kaissis

CoRR, 2021

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

Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Julian O. Matschinske, Jan Baumbach, Daniel Rueckert, Georgios Kaissis

CoRR, 2021

Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience.

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

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation.

Felix Meissen, Georgios Kaissis, Daniel Rueckert

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

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

Benjamin Hou, Georgios Kaissis, Ronald M. Summers, Bernhard Kainz

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.

Alina Dima, Johannes C. Paetzold, Friederike Jungmann, Tristan Lemke, Philipp Raffler, Georgios Kaissis, Daniel Rueckert, Rickmer Braren

Proceedings of the Machine Learning in Medical Imaging - 12th International Workshop, 2021

U-Noise: Learnable Noise Masks for Interpretable Image Segmentation.

Teddy Koker, Fatemehsadat Mireshghallah, Tom Titcombe, Georgios Kaissis

Proceedings of the 2021 IEEE International Conference on Image Processing, 2021

Challenging Current Semi-supervised Anomaly Segmentation Methods for Brain MRI.

Felix Meissen, Georgios Kaissis, Daniel Rueckert

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2021

2020

Secure, privacy-preserving and federated machine learning in medical imaging.

Georgios Kaissis, Marcus R. Makowski, Daniel Rueckert, Rickmer Braren

Nat. Mach. Intell., 2020

Privacy-preserving medical image analysis.

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

CoRR, 2020

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

Moritz Knolle, Georgios Kaissis, Friederike Jungmann, Sebastian Ziegelmayer, Daniel Sasse, Marcus R. Makowski, Daniel Rueckert, Rickmer Braren

CoRR, 2020

2019

The Liver Tumor Segmentation Benchmark (LiTS).

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

CoRR, 2019

2017

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

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

CoRR, 2017

SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D Convolutional Neural Networks.

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

Proceedings of the 14th IEEE International Symposium on Biomedical Imaging, 2017