Abstract Over the years, Machine Learning models have been successfully employed on
neuroimaging data for accurately predicting brain age. Deviations from the healthy brain …

The remarkable flexibility and adaptability of ensemble methods and deep learning models
have led to the proliferation of their application in bioinformatics research. Traditionally …

Deep network architectures struggle to continually learn new tasks without forgetting the
previous tasks. A recent trend indicates that dynamic architectures based on an expansion …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

The precise localization of 3D objects from a single image without depth information is a
highly challenging problem. Most existing methods adopt the same approach for all objects …

Deep ensembles have been empirically shown to be a promising approach for improving
accuracy, uncertainty and out-of-distribution robustness of deep learning models. While …

Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks,
including abstained prediction, out-of-distribution detection, and aleatoric uncertainty …

Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …

Many real-world vision problems suffer from inherent ambiguities. In clinical applications for
example, it might not be clear from a CT scan alone which particular region is cancer tissue …

In this paper we demonstrate the effectiveness of a well trained U-Net in the context of the
BraTS 2018 challenge. This endeavour is particularly interesting given that researchers are …