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Books and Theses
- 2013
[b1]Jonathan Passerat-Palmbach:
Contributions to parallel stochastic simulation: Application of good software engineering practices to the distribution of pseudorandom streams in hybrid Monte-Carlo simulations. (Contributions à la simulation stochastique parallèle: architectures logicielles pour la distribution de flux pseudo-aléatoires dans les simulations Monte Carlo sur CPU/GPU). Blaise Pascal University, Clermont-Ferrand, France, 2013
Journal Articles
- 2022
- [j15]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) - 2021
- [j14]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:
End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nat. Mach. Intell. 3(6): 473-484 (2021) - [j13]Dmitrii Usynin, Alexander Ziller, Marcus R. Makowski, Rickmer Braren, Daniel Rueckert, Ben Glocker, Georgios Kaissis, Jonathan Passerat-Palmbach:
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning. Nat. Mach. Intell. 3(9): 749-758 (2021) - 2020
- [j12]Sean P. Fitzgibbon, Samuel J. Harrison, Mark Jenkinson, Luke Baxter, Emma C. Robinson, Matteo Bastiani, Jelena Bozek, Vyacheslav Karolis, Lucilio Cordero-Grande, Anthony N. Price, Emer J. Hughes, Antonios Makropoulos, Jonathan Passerat-Palmbach, Andreas Schuh, Jianliang Gao, Seyedeh-Rezvan Farahibozorg, Jonathan O'Muircheartaigh, Judit Ciarrusta, Jesper Andersson:
The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants. NeuroImage 223: 117303 (2020) - 2019
- [j11]Giacomo Tarroni, Ozan Oktay, Wenjia Bai, Andreas Schuh, Hideaki Suzuki, Jonathan Passerat-Palmbach, Antonio de Marvao, Declan P. O'Regan, Stuart A. Cook, Ben Glocker, Paul M. Matthews, Daniel Rueckert:
Learning-Based Quality Control for Cardiac MR Images. IEEE Trans. Medical Imaging 38(5): 1127-1138 (2019) - 2018
- [j10]Antonios Makropoulos, Emma C. Robinson, Andreas Schuh, Robert Wright, Sean P. Fitzgibbon, Jelena Bozek, Serena J. Counsell, Johannes K. Steinweg, Katy Vecchiato, Jonathan Passerat-Palmbach, Gregor Lenz, Filippo Mortari, Tencho Tenev, Eugene P. Duff, Matteo Bastiani, Lucilio Cordero-Grande, Emer J. Hughes, Nora Tusor, Daniel Rueckert:
The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction. NeuroImage 173: 88-112 (2018) - [j9]Lisa M. Koch, Martin Rajchl, Wenjia Bai, Christian F. Baumgartner, Tong Tong, Jonathan Passerat-Palmbach, Paul Aljabar, Daniel Rueckert:
Multi-Atlas Segmentation Using Partially Annotated Data: Methods and Annotation Strategies. IEEE Trans. Pattern Anal. Mach. Intell. 40(7): 1683-1696 (2018) - 2017
- [j8]Jonathan Passerat-Palmbach, Romain Reuillon, Mathieu Leclaire, Antonios Makropoulos, Emma C. Robinson, Sarah Parisot, Daniel Rueckert:
Reproducible Large-Scale Neuroimaging Studies with the OpenMOLE Workflow Management System. Frontiers Neuroinformatics 11: 21 (2017) - [j7]Martin Rajchl, Matthew C. H. Lee, Ozan Oktay, Konstantinos Kamnitsas, Jonathan Passerat-Palmbach, Wenjia Bai, Mellisa Damodaram, Mary A. Rutherford, Joseph V. Hajnal, Bernhard Kainz, Daniel Rueckert:
DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks. IEEE Trans. Medical Imaging 36(2): 674-683 (2017) - 2016
- [j6]Sarah Parisot, Salim Arslan, Jonathan Passerat-Palmbach, William M. Wells III, Daniel Rueckert:
Group-wise parcellation of the cortex through multi-scale spectral clustering. NeuroImage 136: 68-83 (2016) - 2015
- [j5]Jonathan Passerat-Palmbach, Claude Mazel, David R. C. Hill:
TaskLocalRandom: a statistically sound substitute to pseudorandom number generation in parallel java tasks frameworks. Concurr. Comput. Pract. Exp. 27(13): 3383-3398 (2015) - [j4]Jonathan Passerat-Palmbach, Jonathan Caux, Pierre Schweitzer, Pridi Siregar, Claude Mazel, David R. C. Hill:
Harnessing aspect-oriented programming on GPU: application to warp-level parallelism. Int. J. Comput. Aided Eng. Technol. 7(2): 158-175 (2015) - 2013
- [j3]David R. C. Hill, Claude Mazel, Jonathan Passerat-Palmbach, Mamadou Kaba Traoré:
Distribution of random streams for simulation practitioners. Concurr. Comput. Pract. Exp. 25(10): 1427-1442 (2013) - 2012
- [j2]Romain Reuillon, Mamadou K. Traoré, Jonathan Passerat-Palmbach, David R. C. Hill:
Parallel stochastic simulations with rigorous distribution of pseudo-random numbers with DistMe: Application to life science simulations. Concurr. Comput. Pract. Exp. 24(7): 723-738 (2012) - [j1]Jonathan Passerat-Palmbach, Claude Mazel, David R. C. Hill:
Pseudo-random streams for distributed and parallel stochastic simulations on GP-GPU. J. Simulation 6(3): 141-151 (2012)
Conference and Workshop Papers
- 2024
- [c17]Vasilis Siomos, Sergio Naval Marimont, Jonathan Passerat-Palmbach, Giacomo Tarroni:
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification. ISBI 2024: 1-5 - 2022
- [c16]Vasilis Siomos, Giacomo Tarroni, Jonathan Passerat-Palmbach:
FeTS Challenge 2022 Task 1: Implementing FedMGDA + and a New Partitioning. BrainLes@MICCAI (2) 2022: 154-160 - 2020
- [c15]Jonathan Passerat-Palmbach, Tyler Farnan, Mike McCoy, Justin D. Harris, Sean T. Manion, Heather Leigh Flannery, Bill Gleim:
Blockchain-orchestrated machine learning for privacy preserving federated learning in electronic health data. Blockchain 2020: 550-555 - [c14]Veneta Haralampieva, Daniel Rueckert, Jonathan Passerat-Palmbach:
A Systematic Comparison of Encrypted Machine Learning Solutions for Image Classification. PPMLP@CCS 2020: 55-59 - 2018
- [c13]Amir Alansary, Loïc Le Folgoc, Ghislain Vaillant, Ozan Oktay, Yuanwei Li, Wenjia Bai, Jonathan Passerat-Palmbach, Ricardo Guerrero, Konstantinos Kamnitsas, Benjamin Hou, Steven G. McDonagh, Ben Glocker, Bernhard Kainz, Daniel Rueckert:
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents. MICCAI (1) 2018: 277-285 - 2017
- [c12]Giacomo Tarroni, Ozan Oktay, Wenjia Bai, Andreas Schuh, Hideaki Suzuki, Jonathan Passerat-Palmbach, Ben Glocker, Antonio de Marvao, Declan P. O'Regan, Stuart A. Cook, Daniel Rueckert:
Learning-Based Heart Coverage Estimation for Short-Axis Cine Cardiac MR Images. FIMH 2017: 73-82 - 2015
- [c11]Romain Reuillon, Mathieu Leclaire, Jonathan Passerat-Palmbach:
Model exploration using OpenMOLE a workflow engine for large scale distributed design of experiments and parameter tuning. HPCS 2015: 1-8 - [c10]Lisa M. Koch, Martin Rajchl, Tong Tong, Jonathan Passerat-Palmbach, Paul Aljabar, Daniel Rueckert:
Multi-atlas Segmentation as a Graph Labelling Problem: Application to Partially Annotated Atlas Data. IPMI 2015: 221-232 - [c9]Sarah Parisot, Salim Arslan, Jonathan Passerat-Palmbach, William M. Wells III, Daniel Rueckert:
Tractography-Driven Groupwise Multi-scale Parcellation of the Cortex. IPMI 2015: 600-612 - [c8]Sarah Parisot, Martin Rajchl, Jonathan Passerat-Palmbach, Daniel Rueckert:
A Continuous Flow-Maximisation Approach to Connectivity-Driven Cortical Parcellation. MICCAI (3) 2015: 165-172 - 2013
- [c7]Jonathan Passerat-Palmbach, Romain Reuillon, Claude Mazel, David R. C. Hill:
Prototyping parallel simulations on manycore architectures using Scala: A case study. HPCS 2013: 405-412 - [c6]Jonathan Passerat-Palmbach, Jonathan Caux, Yannick Le Pennec, Romain Reuillon, Ivan Junier, François Képès, David R. C. Hill:
Parallel stepwise stochastic simulation: harnessing GPUs to explore possible futures states of a chromosome folding model thanks to the possible futures algorithm (PFA). SIGSIM-PADS 2013: 169-178 - 2012
- [c5]Hesham H. Ali, Laura Ricci, Italo Epicoco, Manuel Ujaldon, Jonathan Passerat-Palmbach, David R. C. Hill, Manuel Villén-Altamirano:
HPCS 2012 tutorials: Tutorial I: High performance computing in biomedical informatics. HPCS 2012 - [c4]Jonathan Passerat-Palmbach, David R. C. Hill:
How to correctly deal with pseudorandom numbers in manycore environments: Application to GPU programming with Shoverand. HPCS 2012: 25-31 - [c3]Jonathan Passerat-Palmbach, Claude Mazel, David R. C. Hill:
ThreadLocalMRG32k3a: A statistically sound substitute to pseudorandom number generation in parallel Java applications. HPCS 2012: 543-550 - 2011
- [c2]Jonathan Passerat-Palmbach, Claude Mazel, Bruno Bachelet, David R. C. Hill:
ShoveRand: A model-driven framework to easily generate random numbers on GP-GPU. HPCS 2011: 41-48 - [c1]Jonathan Passerat-Palmbach, Claude Mazel, David R. C. Hill:
Pseudo-Random Number Generation on GP-GPU. PADS 2011: 1-8
Editorship
- 2021
- [e1]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:
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]
Informal and Other Publications
- 2024
- [i27]Bianca-Mihaela Ganescu, Jonathan Passerat-Palmbach:
Trust the Process: Zero-Knowledge Machine Learning to Enhance Trust in Generative AI Interactions. CoRR abs/2402.06414 (2024) - [i26]Vasilis Siomos, Sergio Naval Marimont, Jonathan Passerat-Palmbach, Giacomo Tarroni:
Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration. CoRR abs/2410.02006 (2024) - 2023
- [i25]Xinyuan Sun, Davide Crapis, Matt Stephenson, Barnabé Monnot, Thomas Thiery, Jonathan Passerat-Palmbach:
Cooperative AI via Decentralized Commitment Devices. CoRR abs/2311.07815 (2023) - [i24]Vasilis Siomos, Jonathan Passerat-Palmbach:
Contribution Evaluation in Federated Learning: Examining Current Approaches. CoRR abs/2311.09856 (2023) - [i23]Vasilis Siomos, Sergio Naval Marimont, Jonathan Passerat-Palmbach, Giacomo Tarroni:
ARIA: On the interaction between Architectures, Aggregation methods and Initializations in federated visual classification. CoRR abs/2311.14625 (2023) - 2022
- [i22]George-Liviu Pereteanu, Amir Alansary, Jonathan Passerat-Palmbach:
Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption. CoRR abs/2202.13351 (2022) - 2021
- [i21]Ashly Lau, Jonathan Passerat-Palmbach:
Statistical Privacy Guarantees of Machine Learning Preprocessing Techniques. CoRR abs/2109.02496 (2021) - [i20]Stefán Páll Sturluson, Samuel Trew, Luis Muñoz-González, Matei Grama, Jonathan Passerat-Palmbach, Daniel Rueckert, Amir Alansary:
FedRAD: Federated Robust Adaptive Distillation. CoRR abs/2112.01405 (2021) - [i19]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
- [i18]Matei Grama, Maria Musat, Luis Muñoz-González, Jonathan Passerat-Palmbach, Daniel Rueckert, Amir Alansary:
Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare. CoRR abs/2009.08294 (2020) - [i17]Veneta Haralampieva, Daniel Rueckert, Jonathan Passerat-Palmbach:
A Systematic Comparison of Encrypted Machine Learning Solutions for Image Classification. CoRR abs/2011.05296 (2020) - [i16]Harry Cai, Daniel Rueckert, Jonathan Passerat-Palmbach:
2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments. CoRR abs/2011.07516 (2020) - [i15]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:
Privacy-preserving medical image analysis. CoRR abs/2012.06354 (2020) - 2019
- [i14]Jonathan Passerat-Palmbach, Tyler Farnan, Robert Miller, Marielle S. Gross, Heather Leigh Flannery, Bill Gleim:
A blockchain-orchestrated Federated Learning architecture for healthcare consortia. CoRR abs/1910.12603 (2019) - 2018
- [i13]Giacomo Tarroni, Ozan Oktay, Wenjia Bai, Andreas Schuh, Hideaki Suzuki, Jonathan Passerat-Palmbach, Ben Glocker, Paul M. Matthews, Daniel Rueckert:
Learning-Based Quality Control for Cardiac MR Images. CoRR abs/1803.09354 (2018) - [i12]Amir Alansary, Loïc Le Folgoc, Ghislain Vaillant, Ozan Oktay, Yuanwei Li, Wenjia Bai, Jonathan Passerat-Palmbach, Ricardo Guerrero, Konstantinos Kamnitsas, Benjamin Hou, Steven G. McDonagh, Ben Glocker, Bernhard Kainz, Daniel Rueckert:
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents. CoRR abs/1806.03228 (2018) - [i11]Théo Ryffel, Andrew Trask, Morten Dahl, Bobby Wagner, Jason Mancuso, Daniel Rueckert, Jonathan Passerat-Palmbach:
A generic framework for privacy preserving deep learning. CoRR abs/1811.04017 (2018) - 2017
- [i10]Martin Rajchl, Lisa M. Koch, Christian Ledig, Jonathan Passerat-Palmbach, Kazunari Misawa, Kensaku Mori, Daniel Rueckert:
Employing Weak Annotations for Medical Image Analysis Problems. CoRR abs/1708.06297 (2017) - 2016
- [i9]Lisa M. Koch, Martin Rajchl, Wenjia Bai, Christian F. Baumgartner, Tong Tong, Jonathan Passerat-Palmbach, Paul Aljabar, Daniel Rueckert:
Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies. CoRR abs/1605.00029 (2016) - [i8]Martin Rajchl, Matthew C. H. Lee, Ozan Oktay, Konstantinos Kamnitsas, Jonathan Passerat-Palmbach, Wenjia Bai, Bernhard Kainz, Daniel Rueckert:
DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks. CoRR abs/1605.07866 (2016) - [i7]Martin Rajchl, Matthew C. H. Lee, Franklin Schrans, Alice Davidson, Jonathan Passerat-Palmbach, Giacomo Tarroni, Amir Alansary, Ozan Oktay, Bernhard Kainz, Daniel Rueckert:
Learning under Distributed Weak Supervision. CoRR abs/1606.01100 (2016) - [i6]Sarah Parisot, Jonathan Passerat-Palmbach, Markus D. Schirmer, Boris Gutman:
Proceedings of the Workshop on Brain Analysis using COnnectivity Networks - BACON 2016. CoRR abs/1611.03363 (2016) - [i5]Sofia Ira Ktena, Sarah Parisot, Jonathan Passerat-Palmbach, Daniel Rueckert:
Comparison of Brain Networks with Unknown Correspondences. CoRR abs/1611.04783 (2016) - 2015
- [i4]Jonathan Passerat-Palmbach, Jonathan Caux, Pridi Siregar, Claude Mazel, David R. C. Hill:
Warp-Level Parallelism: Enabling Multiple Replications In Parallel on GPU. CoRR abs/1501.01405 (2015) - [i3]Jonathan Passerat-Palmbach, Claude Mazel, Antoine Mahul, David R. C. Hill:
Reliable Initialization of GPU-enabled Parallel Stochastic Simulations Using Mersenne Twister for Graphics Processors. CoRR abs/1501.07701 (2015) - [i2]Romain Reuillon, Mathieu Leclaire, Jonathan Passerat-Palmbach:
Model Exploration Using OpenMOLE - a workflow engine for large scale distributed design of experiments and parameter tuning. CoRR abs/1506.04182 (2015) - 2014
- [i1]Jonathan Passerat-Palmbach, David R. C. Hill:
How to Correctly Deal With Pseudorandom Numbers in Manycore Environments - Application to GPU programming with Shoverand. CoRR abs/1412.8266 (2014)
Coauthor Index
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