Comparison of semi-supervised learning methods for High Content Screening quality control

TL;DR

This paper evaluates semi-supervised deep learning methods for quality control in high-content screening, finding transfer learning approaches most effective and practical for detecting image artefacts without extensive annotations.

Contribution

It compares recent self-supervised and transfer learning methods for HCS artefact detection, highlighting transfer learning's superior performance and ease of use.

Findings

Transfer learning outperforms self-supervised methods in artefact detection.

Transfer learning requires less hyperparameter tuning and training time.

Semi-supervised approaches improve quality control in high-content screening.

Abstract

Progress in automated microscopy and quantitative image analysis has promoted high-content screening (HCS) as an efficient drug discovery and research tool. While HCS offers to quantify complex cellular phenotypes from images at high throughput, this process can be obstructed by image aberrations such as out-of-focus image blur, fluorophore saturation, debris, a high level of noise, unexpected auto-fluorescence or empty images. While this issue has received moderate attention in the literature, overlooking these artefacts can seriously hamper downstream image processing tasks and hinder detection of subtle phenotypes. It is therefore of primary concern, and a prerequisite, to use quality control in HCS. In this work, we evaluate deep learning options that do not require extensive image annotations to provide a straightforward and easy to use semi-supervised learning solution to this issue. Concretely, we compared the efficacy of recent self-supervised and transfer learning approaches to provide a base encoder to a high throughput artefact image detector. The results of this study suggest that transfer learning methods should be preferred for this task as they not only performed best here but present the advantage of not requiring sensitive hyperparameter settings nor extensive additional training.