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

arXiv: https://arxiv.org/abs/2107.07761 · HTML: https://mlsanity.com/paper/2107.07761/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 44 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 20 of 20)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Which Training Methods for GANs do actually Converge?](https://mlsanity.com/paper/1801.04406/) (2018)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [A Brief Review of Domain Adaptation](https://mlsanity.com/paper/2010.03978/) (2020)
- [On the regularization of Wasserstein GANs](https://mlsanity.com/paper/1709.08894/) (2017)
- [Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention](https://mlsanity.com/paper/2007.08373/) (2020)
- [Self-Supervised Similarity Learning for Digital Pathology](https://mlsanity.com/paper/1905.08139/) (2019)
- [Extending and Analyzing Self-Supervised Learning Across Domains](https://mlsanity.com/paper/2004.11992/) (2020)
- [Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer](https://mlsanity.com/paper/2007.03292/) (2020)
- [MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification](https://mlsanity.com/paper/1612.08879/) (2016)
- [Improving Image Autoencoder Embeddings with Perceptual Loss](https://mlsanity.com/paper/2001.03444/) (2020)
- [Is Discriminator a Good Feature Extractor?](https://mlsanity.com/paper/1912.00789/) (2019)
- [DEEMD: Drug Efficacy Estimation against SARS-CoV-2 based on cell Morphology with Deep multiple instance learning](https://mlsanity.com/paper/2105.05758/) (2021)
- [W2WNet: a two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality](https://mlsanity.com/paper/2103.13107/) (2021)

## Built on by (most cited first, 2 of 2)

- [Multiplexed Immunofluorescence Brain Image Analysis Using Self-Supervised Dual-Loss Adaptive Masked Autoencoder](https://mlsanity.com/paper/2205.05194/) (2022)
- [Renal Cell Carcinoma subtyping: learning from multi-resolution localization](https://mlsanity.com/paper/2411.09471/) (2024)

## Abstract (arXiv)

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.