Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations.

ABSTRACT:

Motivation

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 Generative Adversarial Network Discriminator Learner (GAN-DL), a novel self-supervised learning paradigm based on the StyleGAN2 architecture, which we employ for self-supervised image representation learning in the case of fluorescent biological images.

Results

We show that Wasserstein Generative Adversarial Networks 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 two different cell models: the primary human renal cortical epithelial cells (HRCE) and th

SUBMITTER: Mascolini A 

PROVIDER: S-EPMC9308954 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature