  In recent years, Convolutional Neural Networks (CNNs) have become the
state-of-the-art method for biomedical image analysis. However, these networks
are usually trained in a supervised manner, requiring large amounts of labelled
training data. These labelled data sets are often difficult to acquire in the
biomedical domain. In this work, we validate alternative ways to train CNNs
with fewer labels for biomedical image segmentation using. We adapt two semi-
and self-supervised image classification methods and analyse their performance
for semantic segmentation of biomedical microscopy images.
