Ben Glocker

TL;DR: This study analyzes overfitting by examining how the distribution of logits alters in relation to how much the model overfits, and derives asymmetric modifications of existing loss functions and regularizers including a large margin loss, focal loss, adversarial training and mixup which specifically aim at reducing the shift observed when embedding unseen samples of the under-represented class.

TL;DR: It is shown that combining multiple distances related to the condition improves the overall characterization and classification of the three clinical groups compared to the use of single distances and classical unsupervised manifold learning.

TL;DR: Treatment for patients with acute subdural haematoma with similar characteristics differed depending on the treating centre, because of variation in the preferred approach, and a treatment strategy preferring an aggressive approach of acute surgical evacuation over initial conservative treatment was not associated with better functional outcome.

TL;DR: Quantitative and qualitative results on the Human Connectome Project database show that integrating multi‐modal information yields a stronger agreement with well established atlases and more robust connectivity networks that provide a better representation of the population.