Self-supervised Representation Learning for Cell Event Recognition through Time Arrow Prediction

TL;DR

This paper introduces a self-supervised learning approach using time arrow prediction to improve cell event recognition in live-cell microscopy, reducing the need for extensive annotations and enhancing performance.

Contribution

It presents a novel self-supervised representation learning method based on time arrow prediction for cell event recognition in microscopy data.

Findings

Outperforms fully supervised models with limited annotations

Provides insights into applying SSRL in live-cell microscopy

Demonstrates effectiveness through extensive experiments

Abstract

The spatio-temporal nature of live-cell microscopy data poses challenges in the analysis of cell states which is fundamental in bioimaging. Deep-learning based segmentation or tracking methods rely on large amount of high quality annotations to work effectively. In this work, we explore an alternative solution: using feature maps obtained from self-supervised representation learning (SSRL) on time arrow prediction (TAP) for the downstream supervised task of cell event recognition. We demonstrate through extensive experiments and analysis that this approach can achieve better performance with limited annotation compared to models trained from end to end using fully supervised approach. Our analysis also provides insight into applications of the SSRL using TAP in live-cell microscopy.