Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations: a COVID-19 case-study

TL;DR

This paper introduces GAN-DL, a self-supervised learning method using StyleGAN2 and Wasserstein GANs for biological image analysis, enabling classification and dose-response assessment without extensive annotations, demonstrated on SARS-CoV-2 datasets.

Contribution

The study presents a novel self-supervised approach combining StyleGAN2 and Wasserstein GANs for biological image classification without requiring large annotated datasets.

Findings

High-throughput compound screening achieved with raw images.

Effective classification of SARS-CoV-2 infection inhibition.

Successful zero-shot cell type categorization.

Abstract

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.