  In microscopy, the time burden and cost of acquiring and annotating large
datasets that many deep learning models take as a prerequisite, often appears
to make these methods impractical. Can this requirement for annotated data be
relaxed? Is it possible to borrow the knowledge gathered from datasets in other
application fields and leverage it for microscopy? Here, we aim to provide an
overview of methods that have recently emerged to successfully train
learning-based methods in bio-microscopy.
