Deep Learning (DL) represents a key technological innovation in the field of machine learning. Recent advancements have attracted much attention by showing substantial improvements in a wide range of applications such as image recognition, speech recognition, natural language processing and artificial intelligence. In some cases the performance even surpasses human accuracy, which motivated the introduction of a series of DL-based software products and automatization solutions (for example Apple Siri, Google Now, Google Autonomous Driving etc.). The same success also echoes in the research efforts of the medical imaging community. However, in this case several constraints such as data-availability, inherent data noise or lack of labeled data directly affect the pace of advancements.