  High content imaging assays can capture rich phenotypic response data for
large sets of compound treatments, aiding in the characterization and discovery
of novel drugs. However, extracting representative features from high content
images that can capture subtle nuances in phenotypes remains challenging. The
lack of high-quality labels makes it difficult to achieve satisfactory results
with supervised deep learning. Self-Supervised learning methods have shown
great success on natural images, and offer an attractive alternative also to
microscopy images. However, we find that self-supervised learning techniques
underperform on high content imaging assays. One challenge is the undesirable
domain shifts present in the data known as batch effects, which are caused by
biological noise or uncontrolled experimental conditions. To this end, we
introduce Cross-Domain Consistency Learning (CDCL), a self-supervised approach
that is able to learn in the presence of batch effects. CDCL enforces the
learning of biological similarities while disregarding undesirable
batch-specific signals, leading to more useful and versatile representations.
These features are organised according to their morphological changes and are
more useful for downstream tasks -- such as distinguishing treatments and
mechanism of action.
