J
Jonathan Passerat-Palmbach
Researcher at Imperial College London
Publications - 71
Citations - 2157
Jonathan Passerat-Palmbach is an academic researcher from Imperial College London. The author has contributed to research in topics: Pseudorandom number generator & Connectome. The author has an hindex of 18, co-authored 68 publications receiving 1213 citations. Previous affiliations of Jonathan Passerat-Palmbach include Analysis Group & Blaise Pascal University.
Papers
More filters
Journal ArticleDOI
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 +10 more
TL;DR: DeepCut as discussed by the authors proposes a method to obtain pixelwise object segmentations given an image dataset labeled weak annotations, in our case bounding boxes, by training a neural network classifier from bounding box annotations.
Journal ArticleDOI
The Developing Human Connectome Project: a Minimal Processing Pipeline for Neonatal Cortical Surface Reconstruction
Antonios Makropoulos,Emma C. Robinson,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 Hughes,Nora Tusor,Jacques-Donald Tournier,Jana Hutter,Anthony N. Price,Rui Pedro A. G. Teixeira,Maria Murgasova,Suresh Victor,Christopher Kelly,Mary A. Rutherford,Stephen M. Smith,A. David Edwards,Joseph V. Hajnal,Mark Jenkinson,Daniel Rueckert +31 more
TL;DR: A fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain is proposed, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI.
Posted Content
A generic framework for privacy preserving deep learning
Théo Ryffel,Andrew Trask,Morten Dahl,Bobby Wagner,Jason Mancuso,Daniel Rueckert,Jonathan Passerat-Palmbach +6 more
TL;DR: A new framework for privacy preserving deep learning that allows one to implement complex privacy preserving constructs such as Federated Learning, Secure Multiparty Computation, and Differential Privacy while still exposing a familiar deep learning API to the end-user is detailed.
Journal ArticleDOI
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,Daniel Rueckert,Rickmer Braren,Rickmer Braren +15 more
TL;DR: PriMIA (Privacy-preserving Medical Image Analysis), a free, open-source software framework for differentially private, securely aggregated federated learning and encrypted inference on medical imaging data, is presented.
Book ChapterDOI
PySyft: A Library for Easy Federated Learning
Alexander Ziller,Andrew Trask,Antonio Lopardo,Benjamin Szymkow,Bobby Wagner,Emma Bluemke,Jean-Mickael Nounahon,Jonathan Passerat-Palmbach,Kritika Prakash,Nick Rose,Théo Ryffel,Zarreen Naowal Reza,Georgios Kaissis +12 more
TL;DR: This chapter introduces Duet: the authors' tool for easier FL for scientists and data owners and provides a proof-of-concept demonstration of a FL workflow using an example of how to train a convolutional neural network.