End-to-end privacy preserving deep learning on multi-institutional medical imaging
Georgios Kaissis(TUM Klinikum), Rickmer Braren(Universität Hamburg), Jonathan Passerat‐Palmbach(Imperial College London), Alexander Ziller(TUM Klinikum), Friederike Jungmann(TUM Klinikum), Dmitrii Usynin(TUM Klinikum), Marcus R. Makowski(TUM Klinikum), Théo Ryffel, Jason Mancuso, Ionésio Da Lima(Universidade Federal de Campina Grande), M. Steinborn(München Klinik Schwabing), Andreas Saleh(München Klinik Schwabing), Andrew Trask(Institute on Governance), Daniel Rueckert(Munich Center for Machine Learning)
Cited by 468
Related Papers
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
|Unknown|2016|7.1k
Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data
|NeuroImage|2006|6.6k
Nonrigid registration using free-form deformations: application to breast MR images
|IEEE Transactions on Medical Imaging|1999|5.3k
Attention U-Net: Learning Where to Look for the Pancreas
|arXiv (Cornell University)|2018|4.6k
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
|Medical Image Analysis|2016|3.5k