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High content imaging assays can capture rich phenotypic response data forlarge sets of compound treatments, aiding in the characterization and discoveryof novel drugs. However, extracting representative features from high contentimages that can capture subtle nuances in phenotypes remains challenging. Thelack of high-quality labels makes it difficult to achieve satisfactory resultswith supervised deep learning. Self-Supervised learning methods have showngreat success on natural images, and offer an attractive alternative also tomicroscopy images. However, we find that self-supervised learning techniquesunderperform on high content imaging assays. One challenge is the undesirabledomain shifts present in the data known as batch effects, which are caused bybiological noise or uncontrolled experimental conditions. To this end, weintroduce Cross-Domain Consistency Learning (CDCL), a self-supervised approachthat is able to learn in the presence of batch effects. CDCL enforces thelearning of biological similarities while disregarding undesirablebatch-specific signals, leading to more useful and versatile representations.These features are organised according to their morphological changes and aremore useful for downstream tasks -- such as distinguishing treatments andmechanism of action.

Metadata-guided Consistency Learning for High Content Images