Rewards-based image analysis in microscopy

TL;DR

This paper discusses recent advances in reward-based workflows for microscopy image analysis, emphasizing their potential for autonomous, explainable, and transferable data interpretation across various scientific fields.

Contribution

It introduces reward-driven approaches that shift from traditional supervised methods to explainable, unsupervised optimization in microscopy data analysis, enhancing autonomy and transferability.

Findings

Reward-based workflows capture human reasoning elements.

They enable explainable, unsupervised optimization.

Frameworks are applicable across diverse microscopy tasks.

Abstract

Imaging and hyperspectral data analysis is central to progress across biology, medicine, chemistry, and physics. The core challenge lies in converting high-resolution or high-dimensional datasets into interpretable representations that enable insight into the underlying physical or chemical properties of a system. Traditional analysis relies on expert-designed, multistep workflows, such as denoising, feature extraction, clustering, dimensionality reduction, and physics-based deconvolution, or on machine learning (ML) methods that accelerate individual steps. Both approaches, however, typically demand significant human intervention, including hyperparameter tuning and data labeling. Achieving the next level of autonomy in scientific imaging requires designing effective reward-based workflows that guide algorithms toward best data representation for human or automated decision-making. Here, we discuss recent advances in reward-based workflows for image analysis, which capture key elements of human reasoning and exhibit strong transferability across various tasks. We highlight how reward-driven approaches enable a shift from supervised black-box models toward explainable, unsupervised optimization on the examples of Scanning Probe and Electron Microscopies. Such reward-based frameworks are promising for a broad range of applications, including classification, regression, structure-property mapping, and general hyperspectral data processing.