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AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis
Yutong Chen, Carola-Bibiane Schönlieb, Pietro Liò +6
Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence, deep n…
Distributed Machine Learning and the Semblance of Trust
Dmitrii Usynin, Alexander Ziller, Daniel Rueckert +2
The utilisation of large and diverse datasets for machine learning (ML) at scale is required to promote scientific insight into many meaningful problems. However, due to data gover…
FedRAD: Federated Robust Adaptive Distillation
Stefán Páll Sturluson, Samuel Trew, Luis Muñoz-González +4
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. The collaborative learning…
Embedding Gradient-based Optimization in Image Registration Networks
Huaqi Qiu, Kerstin Hammernik, Chen Qin +2
Deep learning (DL) image registration methods amortize the costly pair-wise iterative optimization by training deep neural networks to predict the optimal transformation in one fas…
Causality-inspired Single-source Domain Generalization for Medical Image Segmentation
Cheng Ouyang, Chen Chen, Surui Li +4
Deep learning models usually suffer from domain shift issues, where models trained on one source domain do not generalize well to other unseen domains. In this work, we investigate…
Complex-valued Federated Learning with Differential Privacy and MRI Applications
Anneliese Riess, Alexander Ziller, Stefan Kolek +3
Federated learning enhanced with Differential Privacy (DP) is a powerful privacy-preserving strategy to protect individuals sharing their sensitive data for processing in fields su…