  Reproducibility is a cornerstone of science, as the replication of findings
is the process through which they become knowledge. It is widely considered
that many fields of science are undergoing a reproducibility crisis. This has
led to the publications of various guidelines in order to improve research
reproducibility.
  This didactic chapter intends at being an introduction to reproducibility for
researchers in the field of machine learning for medical imaging. We first
distinguish between different types of reproducibility. For each of them, we
aim at defining it, at describing the requirements to achieve it and at
discussing its utility. The chapter ends with a discussion on the benefits of
reproducibility and with a plea for a non-dogmatic approach to this concept and
its implementation in research practice.
