  Computer-aided analysis of biological images typically requires extensive
training on large-scale annotated datasets, which is not viable in many
situations. In this paper we present GAN-DL, a Discriminator Learner based on
the StyleGAN2 architecture, which we employ for self-supervised image
representation learning in the case of fluorescent biological images. We show
that Wasserstein Generative Adversarial Networks combined with linear Support
Vector Machines enable high-throughput compound screening based on raw images.
We demonstrate this by classifying active and inactive compounds tested for the
inhibition of SARS-CoV-2 infection in VERO and HRCE cell lines. In contrast to
previous methods, our deep learning based approach does not require any
annotation besides the one that is normally collected during the sample
preparation process. We test our technique on the RxRx19a Sars-CoV-2 image
collection. The dataset consists of fluorescent images that were generated to
assess the ability of regulatory-approved or in late-stage clinical trials
compound to modulate the in vitro infection from SARS-CoV-2 in both VERO and
HRCE cell lines. We show that our technique can be exploited not only for
classification tasks, but also to effectively derive a dose response curve for
the tested treatments, in a self-supervised manner. Lastly, we demonstrate its
generalization capabilities by successfully addressing a zero-shot learning
task, consisting in the categorization of four different cell types of the
RxRx1 fluorescent images collection.
