# A Benchmark for Virus Infection Reporter Virtual Staining in Fluorescence and Brightfield Microscopy

## TL;DR

This paper introduces a benchmark and datasets for virtual staining of virus-infected cells in microscopy images, aiming to improve detection accuracy and detail.

## Contribution

The work introduces a new benchmark and datasets for virus infection reporter virtual staining (VIRVS), enabling better machine learning approaches.

## Key findings

- A benchmark and datasets were created for virus infection virtual staining using diverse viruses and imaging methods.
- U-Net and pix2pix models were tested for VIRVS, showing the feasibility of regressive and generative approaches.
- The study defines a new interdisciplinary challenge at the intersection of data science and virology.

## Abstract

Detecting virus-infected cells in light microscopy requires a reporter signal commonly achieved by immunohistochemistry or genetic engineering. While classification-based machine learning approaches to the detection of virus-infected cells have been proposed, their results lack the nuance of a continuous signal. Such a signal can be achieved by virtual staining. Yet, while this technique has been rapidly growing in importance, the virtual staining of virus-infected cells remains largely uncharted. In this work, we propose a benchmark and datasets to address this. We collate microscopy datasets, containing a panel of viruses of diverse biology and reporters obtained with a variety of magnifications and imaging modalities. Next, we explore the virus infection reporter virtual staining (VIRVS) task employing U-Net and pix2pix architectures as prototypical regressive and generative models. Together our work provides a comprehensive benchmark for VIRVS, as well as defines a new challenge at the interface of Data Science and Virology.