66 citations · 235 across the 19 of their papers we have counts for
3 papers · 1 filter
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…
PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction
Qiang Ma, Emma C. Robinson, Bernhard Kainz +2
Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI). In this work, we introduce Pial Neural Networ…
Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data
Loic Le Folgoc, Vasileios Baltatzis, Amir Alansary +8
Datasets are rarely a realistic approximation of the target population. Say, prevalence is misrepresented, image quality is above clinical standards, etc. This mismatch is known as…