robust training, adversarial examples and what it tells us about modern medical ML classifiers

Undoubtedly, the advent of deep learning for image classification or pattern recognition has created a ecosystem stir in the
medical domain of unprecedented extension. In this talk, I'd like to discuss the question how adversarial examples can help us
quantify the quality of a Deep Learning trained classifyer. With this approach, I'd like to underline how observations and
methods from commercial applications can or cannot be transferred to medical applications. The slidedeck is meant to motivate a discussion on what we expect machine learning to leverage and how this relates to clinical applications with robustness of solutions in mind.