Search Results for author: Jonathan Passerat-Palmbach

Found 18 papers, 1 papers with code

Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption

no code implementations27 Feb 2022 George-Liviu Pereteanu, Amir Alansary, Jonathan Passerat-Palmbach

This work presents a novel protocol for fast secure inference of neural networks applied to computer vision applications.

Model extraction

Distributed Machine Learning and the Semblance of Trust

no code implementations21 Dec 2021 Dmitrii Usynin, Alexander Ziller, Daniel Rueckert, Jonathan Passerat-Palmbach, Georgios Kaissis

The utilisation of large and diverse datasets for machine learning (ML) at scale is required to promote scientific insight into many meaningful problems.

Federated Learning

FedRAD: Federated Robust Adaptive Distillation

no code implementations2 Dec 2021 Stefán Páll Sturluson, Samuel Trew, Luis Muñoz-González, Matei Grama, Jonathan Passerat-Palmbach, Daniel Rueckert, Amir Alansary

The robustness of federated learning (FL) is vital for the distributed training of an accurate global model that is shared among large number of clients.

Federated Learning Knowledge Distillation

Statistical Privacy Guarantees of Machine Learning Preprocessing Techniques

no code implementations6 Sep 2021 Ashly Lau, Jonathan Passerat-Palmbach

Differential privacy provides strong privacy guarantees for machine learning applications.

2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments

no code implementations15 Nov 2020 Harry Cai, Daniel Rueckert, Jonathan Passerat-Palmbach

While the initial model might belong solely to the actor bringing it to the network for training, determining the ownership of the trained model resulting from Federated Learning remains an open question.

Federated Learning

A Systematic Comparison of Encrypted Machine Learning Solutions for Image Classification

no code implementations10 Nov 2020 Veneta Haralampieva, Daniel Rueckert, Jonathan Passerat-Palmbach

This work provides a comprehensive review of existing frameworks based on secure computing techniques in the context of private image classification.

General Classification Image Classification

Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare

no code implementations17 Sep 2020 Matei Grama, Maria Musat, Luis Muñoz-González, Jonathan Passerat-Palmbach, Daniel Rueckert, Amir Alansary

In this work, we implement and evaluate different robust aggregation methods in FL applied to healthcare data.

Cryptography and Security

Learning-Based Quality Control for Cardiac MR Images

no code implementations25 Mar 2018 Giacomo Tarroni, Ozan Oktay, Wenjia Bai, Andreas Schuh, Hideaki Suzuki, Jonathan Passerat-Palmbach, Antonio de Marvao, Declan P. O'Regan, Stuart Cook, Ben Glocker, Paul M. Matthews, Daniel Rueckert

The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e. g. on UK Biobank, sensitivity and specificity respectively 88% and 99% for heart coverage estimation, 85% and 95% for motion detection), allowing their exclusion from the analysed dataset or the triggering of a new acquisition.

Motion Detection

Employing Weak Annotations for Medical Image Analysis Problems

no code implementations21 Aug 2017 Martin Rajchl, Lisa M. Koch, Christian Ledig, Jonathan Passerat-Palmbach, Kazunari Misawa, Kensaku MORI, Daniel Rueckert

To efficiently establish training databases for machine learning methods, collaborative and crowdsourcing platforms have been investigated to collectively tackle the annotation effort.

Computed Tomography (CT) Liver Segmentation

Comparison of Brain Networks with Unknown Correspondences

no code implementations15 Nov 2016 Sofia Ira Ktena, Sarah Parisot, Jonathan Passerat-Palmbach, Daniel Rueckert

In this work we explore a method based on graph edit distance for evaluating graph similarity, when correspondences between network elements are unknown due to different underlying subdivisions of the brain.

Graph Similarity

Proceedings of the Workshop on Brain Analysis using COnnectivity Networks - BACON 2016

no code implementations10 Nov 2016 Sarah Parisot, Jonathan Passerat-Palmbach, Markus D. Schirmer, Boris Gutman

Understanding brain connectivity in a network-theoretic context has shown much promise in recent years.

Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies

no code implementations29 Apr 2016 Lisa M. Koch, Martin Rajchl, Wenjia Bai, Christian F. Baumgartner, Tong Tong, Jonathan Passerat-Palmbach, Paul Aljabar, Daniel Rueckert

Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets.

Cardiac Segmentation

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