ShoveRand: A model-driven framework to easily generate random numbers on GP-GPU. - Jonathan Passerat-Palmbach; Claude Mazel; Bruno Bachelet; David R. C. Hill (HPCS, 2011)

Pseudo-Random Number Generation on GP-GPU. - Jonathan Passerat-Palmbach; Claude Mazel; David R. C. Hill (PADS, 2011)

HPCS 2012 tutorials: Tutorial I: High performance computing in biomedical informatics. - Hesham H. Ali; Laura Ricci; Italo Epicoco; Manuel Ujaldon; Jonathan Passerat-Palmbach; David R. C. Hill; Manuel Villén-Altamirano (HPCS, 2012)

How to correctly deal with pseudorandom numbers in manycore environments: Application to GPU programming with Shoverand. - Jonathan Passerat-Palmbach; David R. C. Hill (HPCS, 2012)

ThreadLocalMRG32k3a: A statistically sound substitute to pseudorandom number generation in parallel Java applications. - Jonathan Passerat-Palmbach; Claude Mazel; David R. C. Hill (HPCS, 2012)

Parallel stochastic simulations with rigorous distribution of pseudo-random numbers with DistMe: Application to life science simulations. - Romain Reuillon; Mamadou K. Traoré; Jonathan Passerat-Palmbach; David R. C. Hill (Concurr. Comput. Pract. Exp., 2012)

Pseudo-random streams for distributed and parallel stochastic simulations on GP-GPU. - Jonathan Passerat-Palmbach; Claude Mazel; David R. C. Hill (J. Simulation, 2012)

Prototyping parallel simulations on manycore architectures using Scala: A case study. - Jonathan Passerat-Palmbach; Romain Reuillon; Claude Mazel; David R. C. Hill (HPCS, 2013)

Parallel stepwise stochastic simulation: harnessing GPUs to explore possible futures states of a chromosome folding model thanks to the possible futures algorithm (PFA). - Jonathan Passerat-Palmbach; Jonathan Caux; Yannick Le Pennec; Romain Reuillon; Ivan Junier; François Képès; David R. C. Hill (SIGSIM-PADS, 2013)

Distribution of random streams for simulation practitioners. - David R. C. Hill; Claude Mazel; Jonathan Passerat-Palmbach; Mamadou Kaba Traoré (Concurr. Comput. Pract. Exp., 2013)

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). - Jonathan Passerat-Palmbach (2013)

Model exploration using OpenMOLE a workflow engine for large scale distributed design of experiments and parameter tuning. - Romain Reuillon; Mathieu Leclaire; Jonathan Passerat-Palmbach (HPCS, 2015)

Multi-atlas Segmentation as a Graph Labelling Problem: Application to Partially Annotated Atlas Data. - Lisa M. Koch; Martin Rajchl; Tong Tong 001; Jonathan Passerat-Palmbach; Paul Aljabar; Daniel Rueckert (IPMI, 2015)

Tractography-Driven Groupwise Multi-scale Parcellation of the Cortex. - Sarah Parisot; Salim Arslan; Jonathan Passerat-Palmbach; William M. Wells III; Daniel Rueckert (IPMI, 2015)

A Continuous Flow-Maximisation Approach to Connectivity-Driven Cortical Parcellation. - Sarah Parisot; Martin Rajchl; Jonathan Passerat-Palmbach; Daniel Rueckert (MICCAI (3), 2015)

TaskLocalRandom: a statistically sound substitute to pseudorandom number generation in parallel java tasks frameworks. - Jonathan Passerat-Palmbach; Claude Mazel; David R. C. Hill (Concurr. Comput. Pract. Exp., 2015)

Warp-Level Parallelism: Enabling Multiple Replications In Parallel on GPU. - Jonathan Passerat-Palmbach; Jonathan Caux; Pridi Siregar; Claude Mazel; David R. C. Hill (CoRR, 2015)

Reliable Initialization of GPU-enabled Parallel Stochastic Simulations Using Mersenne Twister for Graphics Processors. - Jonathan Passerat-Palmbach; Claude Mazel; Antoine Mahul; David R. C. Hill (CoRR, 2015)

Harnessing aspect-oriented programming on GPU: application to warp-level parallelism. - Jonathan Passerat-Palmbach; Jonathan Caux; Pierre Schweitzer; Pridi Siregar; Claude Mazel; David R. C. Hill (Int. J. Comput. Aided Eng. Technol., 2015)

Comparison of Brain Networks with Unknown Correspondences. - Sofia Ira Ktena; Sarah Parisot; Jonathan Passerat-Palmbach; Daniel Rueckert (CoRR, 2016)

Proceedings of the Workshop on Brain Analysis using COnnectivity Networks - BACON 2016. - Sarah Parisot; Jonathan Passerat-Palmbach; Markus D. Schirmer; Boris Gutman (CoRR, 2016)

DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks. - Martin Rajchl; Matthew C. H. Lee; Ozan Oktay; Konstantinos Kamnitsas; Jonathan Passerat-Palmbach; Wenjia Bai; Bernhard Kainz; Daniel Rueckert (CoRR, 2016)

Learning under Distributed Weak Supervision. - Martin Rajchl; Matthew C. H. Lee; Franklin Schrans; Alice Davidson; Jonathan Passerat-Palmbach; Giacomo Tarroni; Amir Alansary; Ozan Oktay; Bernhard Kainz; Daniel Rueckert (CoRR, 2016)

Group-wise parcellation of the cortex through multi-scale spectral clustering. - Sarah Parisot; Salim Arslan; Jonathan Passerat-Palmbach; William M. Wells III; Daniel Rueckert (NeuroImage, 2016)

Learning-Based Heart Coverage Estimation for Short-Axis Cine Cardiac MR Images. - 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 (FIMH, 2017)

Employing Weak Annotations for Medical Image Analysis Problems. - Martin Rajchl; Lisa M. Koch; Christian Ledig; Jonathan Passerat-Palmbach; Kazunari Misawa; Kensaku Mori; Daniel Rueckert (CoRR, 2017)

Reproducible Large-Scale Neuroimaging Studies with the OpenMOLE Workflow Management System. - Jonathan Passerat-Palmbach; Romain Reuillon; Mathieu Leclaire; Antonios Makropoulos; Emma C. Robinson; Sarah Parisot; Daniel Rueckert (Frontiers Neuroinformatics, 2017)

DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks. - 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 (IEEE Trans. Medical Imaging, 2017)

Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents. - Amir Alansary; Loïc Le Folgoc; Ghislain Vaillant; Ozan Oktay; Yuanwei Li; Wenjia Bai; Jonathan Passerat-Palmbach; Ricardo Guerrero; Konstantinos Kamnitsas; Benjamin Hou; Steven McDonagh 001; Ben Glocker; et al. (MICCAI (1), 2018)

Learning-Based Quality Control for Cardiac MR Images. - Giacomo Tarroni; Ozan Oktay; Wenjia Bai; Andreas Schuh; Hideaki Suzuki; Jonathan Passerat-Palmbach; Ben Glocker; Paul M. Matthews; Daniel Rueckert (CoRR, 2018)

A generic framework for privacy preserving deep learning. - Théo Ryffel; Andrew Trask; Morten Dahl; Bobby Wagner; Jason Mancuso; Daniel Rueckert; Jonathan Passerat-Palmbach (CoRR, 2018)

The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction. - 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; et al. (NeuroImage, 2018)

Multi-Atlas Segmentation Using Partially Annotated Data: Methods and Annotation Strategies. - Lisa M. Koch; Martin Rajchl; Wenjia Bai; Christian F. Baumgartner; Tong Tong 001; Jonathan Passerat-Palmbach; Paul Aljabar; Daniel Rueckert (IEEE Trans. Pattern Anal. Mach. Intell., 2018)

A blockchain-orchestrated Federated Learning architecture for healthcare consortia. - Jonathan Passerat-Palmbach; Tyler Farnan; Robert Miller; Marielle S. Gross; Heather Leigh Flannery; Bill Gleim (CoRR, 2019)

Learning-Based Quality Control for Cardiac MR Images. - 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 (IEEE Trans. Medical Imaging, 2019)

Blockchain-orchestrated machine learning for privacy preserving federated learning in electronic health data. - Jonathan Passerat-Palmbach; Tyler Farnan; Mike McCoy; Justin D. Harris; Sean T. Manion; Heather Leigh Flannery; Bill Gleim (Blockchain, 2020)

A Systematic Comparison of Encrypted Machine Learning Solutions for Image Classification. - Veneta Haralampieva; Daniel Rueckert; Jonathan Passerat-Palmbach (PPMLP@CCS, 2020)

Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare. - Matei Grama; Maria Musat; Luis Muñoz-González; Jonathan Passerat-Palmbach; Daniel Rueckert; Amir Alansary (CoRR, 2020)

2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments. - Harry Cai; Daniel Rueckert; Jonathan Passerat-Palmbach (CoRR, 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)

The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants. - 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; et al. (NeuroImage, 2020)

Statistical Privacy Guarantees of Machine Learning Preprocessing Techniques. - Ashly Lau; Jonathan Passerat-Palmbach (CoRR, 2021)

FedRAD: Federated Robust Adaptive Distillation. - Stefán Páll Sturluson; Samuel Trew; Luis Muñoz-González; Matei Grama; Jonathan Passerat-Palmbach; Daniel Rueckert; Amir Alansary (CoRR, 2021)

Distributed Machine Learning and the Semblance of Trust. - Dmitrii Usynin; Alexander Ziller; Daniel Rueckert; Jonathan Passerat-Palmbach; Georgios Kaissis (CoRR, 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; et al. (Nat. Mach. Intell., 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)

FeTS Challenge 2022 Task 1: Implementing FedMGDA + and a New Partitioning. - Vasilis Siomos; Giacomo Tarroni; Jonathan Passerat-Palmbach (BrainLes@MICCAI (2), 2022)

Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption. - George-Liviu Pereteanu; Amir Alansary; Jonathan Passerat-Palmbach (CoRR, 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)

Cooperative AI via Decentralized Commitment Devices. - Xinyuan Sun; Davide Crapis; Matt Stephenson; Barnabé Monnot; Thomas Thiery; Jonathan Passerat-Palmbach (CoRR, 2023)

Contribution Evaluation in Federated Learning: Examining Current Approaches. - Vasilis Siomos; Jonathan Passerat-Palmbach (CoRR, 2023)

ARIA: On the interaction between Architectures, Aggregation methods and Initializations in federated visual classification. - Vasilis Siomos; Sergio Naval Marimont; Jonathan Passerat-Palmbach; Giacomo Tarroni (CoRR, 2023)

ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification. - Vasilis Siomos; Sergio Naval Marimont; Jonathan Passerat-Palmbach; Giacomo Tarroni (ISBI, 2024)

Trust the Process: Zero-Knowledge Machine Learning to Enhance Trust in Generative AI Interactions. - Bianca-Mihaela Ganescu; Jonathan Passerat-Palmbach (CoRR, 2024)

Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration. - Vasilis Siomos; Sergio Naval Marimont; Jonathan Passerat-Palmbach; Giacomo Tarroni (CoRR, 2024)

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models. - Olivia Ma; Jonathan Passerat-Palmbach; Dmitrii Usynin (CoRR, 2024)

Narrowing the Gap between TEEs Threat Model and Deployment Strategies. - Filip Rezabek; Jonathan Passerat-Palmbach; Moe Mahhouk; Frieder Erdmann; Andrew Miller (CoRR, 2025)

FedCLAM: Client Adaptive Momentum with Foreground Intensity Matching for Federated Medical Image Segmentation. - Vasilis Siomos; Jonathan Passerat-Palmbach; Giacomo Tarroni (CoRR, 2025)