Self-Supervised Representation Learning for High-Content Screening

Biopharma drug discovery requires a set of approaches to find, produce, and test the safety of drugs for clinical application. A crucial part involves image-based screening of cell culture models where single cells are stained with appropriate markers to visually distinguish between disease and healthy states. In practice, such image-based screening experiments are frequently performed using highly scalable and automated multichannel microscopy instruments. This automation enables parallel screening against large panels of marketed drugs with known function. However, the large data volume produced by such instruments hinders a systematic inspection by human experts, which consequently leads to an extensive and biased data curation process for supervised phenotypic endpoint classification. To overcome this limitation, we propose a novel approach for learning an embedding of phenotypic endpoints, without any supervision. We employ the concept of archetypal analysis, in which pseudo-labels are extracted based on biologically reasonable endpoints. Subsequently, we use a self-supervised triplet network to learn a phenotypic embedding which is used for visual inspection and top-down assay quality control. Extensive experiments on two industry-relevant assays demonstrate that our method outperforms state-of-the-art unsupervised and supervised approaches.