Ben Glocker

TL;DR: The discriminative approach is based on decision forests using context-aware spatial features, and integrates a generative model of tissue appearance, by using the probabilities obtained by tissue-specific Gaussian mixture models as additional input for the forest.

TL;DR: This work investigates the challenging problem of domain generalization, i.e., training a model on multi-domain source data such that it can directly generalize to target domains with unknown statistics, and adopts a model-agnostic learning paradigm with gradient-based meta-train and meta-test procedures to expose the optimization to domain shift.

TL;DR: The Ensembles of Multiple Models and Architectures (EMMA) as mentioned in this paper was proposed to reduce the influence of the meta-parameters of individual models and the risk of overfitting the configuration to a particular database.

TL;DR: DeepMedic, a 3D CNN architecture previously presented for lesion segmentation, is employed, which is further improved by adding residual connections, aiming to shed some light on requirements for employing such a system.

TL;DR: In this article, Peul et al. presented a study of the CENTER-TBI Participants and Investigators until 1 November 2019, which is free to read and download at: https://authors.elsevier.com/c/1ZjYd5FFzKkkIst